"""
Climate contour/vector plots using cf-python, matplotlib and cartopy.
Andy Heaps NCAS-CMS November 2023
"""
import numpy as np
import subprocess
from scipy import interpolate
import matplotlib
from copy import deepcopy
import os
import sys
import matplotlib.pyplot as plot
from matplotlib.collections import PolyCollection
from distutils.version import StrictVersion
import cartopy
import cartopy.crs as ccrs
import cartopy.util as cartopy_util
import cartopy.feature as cfeature
from scipy.interpolate import griddata
import shapely.geometry as sgeom
import shapely
from matplotlib.collections import PatchCollection
import matplotlib.patches as mpatches
# Check for the minimum cf-python version
cf_version_min = '3.0.0b2'
errstr = '\n\n cf-python > ' + cf_version_min
errstr += '\n needs to be installed to use cf-plot \n\n'
try:
import cf
if StrictVersion(cf.__version__) < StrictVersion(cf_version_min):
raise Warning(errstr)
except ImportError:
raise Warning(errstr)
# Initiate the pvars class
# This is used for storing plotting variables in cfp.plotvars
class pvars(object):
def __init__(self, **kwargs):
'''Initialize a new Pvars instance'''
for attr, value in kwargs.items():
setattr(self, attr, value)
def __str__(self):
'''x.__str__() <==> str(x)'''
a = None
v = None
out = ['%s = %s' % (a, repr(v))]
for a, v in self.__dict__.items():
return '\n'.join(out)
# Check for a display and use the Agg backing store if none is present
# This is for batch mode processing
try:
disp = os.environ["DISPLAY"]
except Exception:
matplotlib.use('Agg')
# Check for user setting of pre_existing_data_dir pointing to central cartopy setup
# This is used in the cfview simple setup process
try:
pre_existing_data_dir = os.environ["pre_existing_data_dir"]
cartopy.config["pre_existing_data_dir"] = pre_existing_data_dir
except:
pass
# Code to check if the ImageMagick display command is available
def which(program):
def is_exe(fpath):
return os.path.exists(fpath) and os.access(fpath, os.X_OK)
def ext_candidates(fpath):
yield fpath
for ext in os.environ.get("PATHEXT", "").split(os.pathsep):
yield fpath + ext
for path in os.environ["PATH"].split(os.pathsep):
exe_file = os.path.join(path, program)
for candidate in ext_candidates(exe_file):
if is_exe(candidate):
return candidate
return None
# Default colour scales
# cscale1 is a differential data scale - blue to red
cscale1 = ['#0a3278', '#0f4ba5', '#1e6ec8', '#3ca0f0', '#50b4fa', '#82d2ff',
'#a0f0ff', '#c8faff', '#e6ffff', '#fffadc', '#ffe878', '#ffc03c',
'#ffa000', '#ff6000', '#ff3200', '#e11400', '#c00000', '#a50000']
# viridis is a continuous data scale - blue, green, yellow
viridis = ['#440154', '#440255', '#440357', '#450558', '#45065a', '#45085b',
'#46095c', '#460b5e', '#460c5f', '#460e61', '#470f62', '#471163',
'#471265', '#471466', '#471567', '#471669', '#47186a', '#48196b',
'#481a6c', '#481c6e', '#481d6f', '#481e70', '#482071', '#482172',
'#482273', '#482374', '#472575', '#472676', '#472777', '#472878',
'#472a79', '#472b7a', '#472c7b', '#462d7c', '#462f7c', '#46307d',
'#46317e', '#45327f', '#45347f', '#453580', '#453681', '#443781',
'#443982', '#433a83', '#433b83', '#433c84', '#423d84', '#423e85',
'#424085', '#414186', '#414286', '#404387', '#404487', '#3f4587',
'#3f4788', '#3e4888', '#3e4989', '#3d4a89', '#3d4b89', '#3d4c89',
'#3c4d8a', '#3c4e8a', '#3b508a', '#3b518a', '#3a528b', '#3a538b',
'#39548b', '#39558b', '#38568b', '#38578c', '#37588c', '#37598c',
'#365a8c', '#365b8c', '#355c8c', '#355d8c', '#345e8d', '#345f8d',
'#33608d', '#33618d', '#32628d', '#32638d', '#31648d', '#31658d',
'#31668d', '#30678d', '#30688d', '#2f698d', '#2f6a8d', '#2e6b8e',
'#2e6c8e', '#2e6d8e', '#2d6e8e', '#2d6f8e', '#2c708e', '#2c718e',
'#2c728e', '#2b738e', '#2b748e', '#2a758e', '#2a768e', '#2a778e',
'#29788e', '#29798e', '#287a8e', '#287a8e', '#287b8e', '#277c8e',
'#277d8e', '#277e8e', '#267f8e', '#26808e', '#26818e', '#25828e',
'#25838d', '#24848d', '#24858d', '#24868d', '#23878d', '#23888d',
'#23898d', '#22898d', '#228a8d', '#228b8d', '#218c8d', '#218d8c',
'#218e8c', '#208f8c', '#20908c', '#20918c', '#1f928c', '#1f938b',
'#1f948b', '#1f958b', '#1f968b', '#1e978a', '#1e988a', '#1e998a',
'#1e998a', '#1e9a89', '#1e9b89', '#1e9c89', '#1e9d88', '#1e9e88',
'#1e9f88', '#1ea087', '#1fa187', '#1fa286', '#1fa386', '#20a485',
'#20a585', '#21a685', '#21a784', '#22a784', '#23a883', '#23a982',
'#24aa82', '#25ab81', '#26ac81', '#27ad80', '#28ae7f', '#29af7f',
'#2ab07e', '#2bb17d', '#2cb17d', '#2eb27c', '#2fb37b', '#30b47a',
'#32b57a', '#33b679', '#35b778', '#36b877', '#38b976', '#39b976',
'#3bba75', '#3dbb74', '#3ebc73', '#40bd72', '#42be71', '#44be70',
'#45bf6f', '#47c06e', '#49c16d', '#4bc26c', '#4dc26b', '#4fc369',
'#51c468', '#53c567', '#55c666', '#57c665', '#59c764', '#5bc862',
'#5ec961', '#60c960', '#62ca5f', '#64cb5d', '#67cc5c', '#69cc5b',
'#6bcd59', '#6dce58', '#70ce56', '#72cf55', '#74d054', '#77d052',
'#79d151', '#7cd24f', '#7ed24e', '#81d34c', '#83d34b', '#86d449',
'#88d547', '#8bd546', '#8dd644', '#90d643', '#92d741', '#95d73f',
'#97d83e', '#9ad83c', '#9dd93a', '#9fd938', '#a2da37', '#a5da35',
'#a7db33', '#aadb32', '#addc30', '#afdc2e', '#b2dd2c', '#b5dd2b',
'#b7dd29', '#bade27', '#bdde26', '#bfdf24', '#c2df22', '#c5df21',
'#c7e01f', '#cae01e', '#cde01d', '#cfe11c', '#d2e11b', '#d4e11a',
'#d7e219', '#dae218', '#dce218', '#dfe318', '#e1e318', '#e4e318',
'#e7e419', '#e9e419', '#ece41a', '#eee51b', '#f1e51c', '#f3e51e',
'#f6e61f', '#f8e621', '#fae622', '#fde724']
# Read in defaults if they exist and overlay
# for contour options of fill, blockfill and lines
global_fill = True
global_lines = True
global_blockfill = False
global_degsym = False
global_viewer = 'display'
defaults_file = os.path.expanduser("~") + '/.cfplot_defaults'
if os.path.exists(defaults_file):
with open(defaults_file) as file:
for line in file:
vals = line.split(' ')
com, val = vals
v = False
if val.strip() == 'True':
v = True
if com == 'blockfill':
global_blockfill = v
if com == 'lines':
global_lines = v
if com == 'fill':
global_fill = v
if com == 'degsym':
global_degsym = v
if com == 'viewer':
global_viewer = val.strip()
# plotvars - global plotting variables
plotvars = pvars(lonmin=-180, lonmax=180, latmin=-90, latmax=90, proj='cyl',
resolution='110m', plot_type=1, boundinglat=0, lon_0=0,
levels=None,
levels_min=None, levels_max=None, levels_step=None,
norm=None, levels_extend='both', xmin=None,
xmax=None, ymin=None, ymax=None, xlog=None, ylog=None,
rows=1, columns=1, file=None, orientation='landscape',
user_mapset=0, user_gset=0, cscale_flag=0, user_levs=0,
user_plot=0, master_plot=None, plot=None, cs=cscale1,
cs_user='cscale1', mymap=None, xticks=None, yticks=None,
xticklabels=None, yticklabels=None, xstep=None, ystep=None,
xlabel=None, ylabel=None, title=None, title_fontsize=15,
axis_label_fontsize=11, text_fontsize=11,
text_fontweight='normal', axis_label_fontweight='normal',
colorbar_fontsize=11, colorbar_fontweight='normal',
title_fontweight='normal', continent_thickness=None,
continent_color=None, continent_linestyle=None,
pos=1, viewer=global_viewer, global_viewer=global_viewer,
tspace_year=None, tspace_month=None, tspace_day=None,
tspace_hour=None, xtick_label_rotation=0,
xtick_label_align='center', ytick_label_rotation=0,
ytick_label_align='right', legend_text_size=11,
legend_text_weight='normal',
cs_uniform=True, master_title=None,
master_title_location=[0.5, 0.95], master_title_fontsize=30,
master_title_fontweight='normal', dpi=None,
plot_xmin=None, plot_xmax=None, plot_ymin=None,
plot_ymax=None, land_color=None, ocean_color=None,
lake_color=None, feature_zorder=99, twinx=False, twiny=False,
rotated_grid_thickness=2.0, rotated_grid_spacing=10,
rotated_deg_spacing=0.75, rotated_continents=True,
rotated_grid=True, rotated_labels=True,
legend_frame=True, legend_frame_edge_color='k',
legend_frame_face_color=None, degsym=global_degsym,
axis_width=None, grid_x_spacing=60, grid_y_spacing=30,
grid_colour='k', grid_linestyle='--', grid_zorder=100,
grid_thickness=1.0, aspect='equal',
graph_xmin=None, graph_xmax=None,
graph_ymin=None, graph_ymax=None,
level_spacing=None, tight=False, gpos_called=False,
titles_con_called=False)
# Check for iPython notebook inline
# and set the viewer to None if found
is_inline = 'inline' in matplotlib.get_backend()
if is_inline:
plotvars.viewer = None
# Check for OSX and if so use matplotlib for for the viewer
# Not all users will have ImageMagick installed / XQuartz running
# Users can still select this with cfp.setvars(viewer='display')
if sys.platform == 'darwin':
plotvars.global_viewer = 'matplotlib'
plotvars.viewer = 'matplotlib'
[docs]
def con(f=None, x=None, y=None, fill=global_fill, lines=global_lines, line_labels=True,
title=None, colorbar_title=None, colorbar=True,
colorbar_label_skip=None, ptype=0, negative_linestyle='solid',
blockfill=global_blockfill, zero_thick=False, colorbar_shrink=None,
colorbar_orientation=None, colorbar_position=None, xlog=False,
ylog=False, axes=True, xaxis=True, yaxis=True, xticks=None,
xticklabels=None, yticks=None, yticklabels=None, xlabel=None,
ylabel=None, colors='k', swap_axes=False, verbose=None,
linewidths=None, alpha=1.0, colorbar_text_up_down=False,
colorbar_fontsize=None, colorbar_fontweight=None,
colorbar_text_down_up=False, colorbar_drawedges=True,
colorbar_fraction=None, colorbar_thick=None,
colorbar_anchor=None, colorbar_labels=None,
linestyles=None, zorder=1, level_spacing=None,
irregular=None, face_lons=False, face_lats=False, face_connectivity=False,
titles=False, mytest=False, transform_first=None, blockfill_fast=None,
nlevs=False, orca=None, orca_skip=None, grid=False):
"""
| con is the interface to contouring in cf-plot. The minimum use is con(f)
| where f is a 2 dimensional array. If a cf field is passed then an
| appropriate plot will be produced i.e. a longitude-latitude or
| latitude-height plot for example. If a 2d numeric array is passed then
| the optional arrays x and y can be used to describe the x and y data
| point locations.
|
| f - array to contour
| x - x locations of data in f (optional)
| y - y locations of data in f (optional)
| fill=True - colour fill contours
| lines=True - draw contour lines and labels
| line_labels=True - label contour lines
| title=title - title for the plot
| ptype=0 - plot type - not needed for cf fields.
| 0 = no specific plot type,
| 1 = longitude-latitude,
| 2 = latitude - height,
| 3 = longitude - height,
| 4 = latitude - time,
| 5 = longitude - time
| 6 = rotated pole
| negative_linestyle='solid' - set to one of 'solid', 'dashed'
| zero_thick=False - add a thick zero contour line. Set to 3 for example.
| blockfill=False - set to True for a blockfill plot
| colorbar_title=colbar_title - title for the colour bar
| colorbar=True - add a colour bar if a filled contour plot
| colorbar_label_skip=None - skip colour bar labels. Set to 2 to skip
| every other label.
| colorbar_orientation=None - options are 'horizontal' and 'vertical'
| The default for most plots is horizontal but
| for polar stereographic plots this is vertical.
| colorbar_shrink=None - value to shrink the colorbar by. If the colorbar
| exceeds the plot area then values of 1.0, 0.55
| or 0.5m may help it better fit the plot area.
| colorbar_position=None - position of colorbar
| [xmin, ymin, x_extent,y_extent] in normalised
| coordinates. Use when a common colorbar
| is required for a set of plots. A typical set
| of values would be [0.1, 0.05, 0.8, 0.02]
| colorbar_fontsize=None - text size for colorbar labels and title
| colorbar_fontweight=None - font weight for colorbar labels and title
| colorbar_text_up_down=False - if True horizontal colour bar labels alternate
| above (start) and below the colour bar
| colorbar_text_down_up=False - if True horizontal colour bar labels alternate
| below (start) and above the colour bar
| colorbar_drawedges=True - draw internal divisions in the colorbar
| colorbar_fraction=None - space for the colorbar - default = 0.21, in normalised
| coordinates
| colorbar_thick=None - thickness of the colorbar - default = 0.015, in normalised
| coordinates
| colorbar_anchor=None - default=0.5 - anchor point of colorbar within the fraction space.
| 0.0 = close to plot, 1.0 = further away
| colorbar_labels=None - labels to use for colorbar. The default is to use the contour
| levels as labels
| colorbar_text_up_down=False - on a horizontal colorbar alternate the
| labels top and bottom starting in the up position
| colorbar_text_down_up=False - on a horizontal colorbar alternate the
| labels bottom and top starting in the bottom position
| colorbar_drawedges=True - draw internal delimeter lines in the colorbar
| colors='k' - contour line colors - takes one or many values.
| xlog=False - logarithmic x axis
| ylog=False - logarithmic y axis
| axes=True - plot x and y axes
| xaxis=True - plot xaxis
| yaxis=True - plot y axis
| xticks=None - xtick positions
| xticklabels=None - xtick labels
| yticks=None - y tick positions
| yticklabels=None - ytick labels
| xlabel=None - label for x axis
| ylabel=None - label for y axis
| swap_axes=False - swap plotted axes - only valid for X, Y, Z vs T plots
| verbose=None - change to 1 to get a verbose listing of what con
| is doing
| linewidths=None - contour linewidths. Either a single number for all
| lines or array of widths
| linestyles=None - takes 'solid', 'dashed', 'dashdot' or 'dotted'
| alpha=1.0 - transparency setting. The default is no transparency.
| zorder=1 - order of drawing
| level_spacing=None - Default of 'linear' level spacing. Also takes 'log', 'loglike',
| 'outlier' and 'inspect'
| irregular=None - flag for contouring irregular data
| face_lons=None - longitude points for face vertices
| face_lats=None - latitude points for face verticies
| face_connectivity=None - connectivity for face verticies
| titles=False - set to True to have a dimensions title
| transform_first=None - Cartopy should transform the points before calling the contouring algorithm,
| which can have a significant impact on speed (it is much faster to transform
| points than it is to transform patches) If this is unset and the number of points
| in the x direction is > 400 then it is set to True.
| blockfill_fast=None - Use pcolormesh blockfill. This is possibly less reliable that the usual code but is
| faster for higher resolution datasets
| nlevs=False - Let Matplotlib work out the levels for the contour plot
| orca=None - User specifies this is an orca tripolar grid. Internally cf-plot tries to detect this by looking
| for a single discontinuity in the logitude 2D array. If found a fix it make to the longitudes so
| that they are no longer discontinuous.
| orca_skip=None - Only plot every nth grid point in the 2D longitude and latitude arrays. This is useful for when
| plotting his resolution data over the whole globe which would otherwise be very slow to visualize.
| grid=False - Draw a grid on the map using the parameters set by cfp.setvars. Defaults are grid_x_spacing=60,
| grid_y_spacing=30, grid_colour='k', grid_linestyle = '--', grid_thickness=1.0
|
:Returns:
None
"""
# Turn off divide warning in contour routine which is a numpy issue
old_settings = np.seterr(all='ignore')
np.seterr(divide='ignore')
# Set potential user axis labels
user_xlabel = xlabel
user_ylabel = ylabel
# Set blockfill to True if blockfill_fast is not None
if blockfill_fast is not None:
blockfill=True
# Check if the field is a CF ugrid field with cell faces
blockfill_ugrid = False
if isinstance(f, cf.Field) and blockfill:
if f.domain_topologies():
if f.domain_topology('cell:face', default=None) is not None:
face_lons_array = f.aux('X').bounds.array
face_lats_array = f.aux('Y').bounds.array
face_connectivity_array = f.domain_topology('cell:face').array
blockfill_ugrid = True
fill = False
lines = False
irregular = True
else:
errstr = '\n\nError - field does not contain the UGRID face information to plot a blockfill plot\n\n\n'
raise TypeError(errstr)
# Set blockfill_2d if blockfill and x and y are 2D
blockfill_2d = False
if blockfill and not isinstance(f, cf.Field):
if np.ndim(x) == 2 and np.ndim(y) == 2:
blockfill_2d = True
# Call gpos(1) if not already called
if plotvars.rows > 1 or plotvars.columns > 1:
if plotvars.gpos_called is False:
gpos(1)
# Extract required data for contouring
# If a cf-python field
if isinstance(f, cf.Field):
ndims = np.squeeze(f.data).ndim
if ndims > 2:
errstr = "\n\ncfp.con error need a 1 or 2 dimensional field to contour\n"
errstr += "received " + str(np.squeeze(f.data).ndim) + " dimensions\n\n"
errstr += str(f)
raise TypeError(errstr)
# Extract data
if verbose:
print('con - calling cf_data_assign')
# Subset the data if a user map is set
# This is use to speed up the plotting
# myfield is used for calculating the contour levels
# myfield_extended is used to make the contour plot
if plotvars.user_mapset:
if plotvars.proj == 'npstere':
f = f.subspace(Y = cf.wi(plotvars.boundinglat, 90.0))
if plotvars.proj == 'spstere':
f = f.subspace(Y = cf.wi(-90.0, plotvars.boundinglat))
# Extract the data
field, x, y, ptype, colorbar_title, xlabel, ylabel, xpole, ypole =\
cf_data_assign(f, colorbar_title, verbose=verbose)
if user_xlabel is not None:
xlabel = user_xlabel
if user_ylabel is not None:
ylabel = user_ylabel
elif isinstance(f, cf.FieldList):
raise TypeError("\n\ncfp.con - cannot contour a field list\n\n")
else:
if verbose:
print('con - using user assigned data')
field = f # field data passed in as f
if x is None:
x = np.arange(np.shape(field)[1])
if y is None:
y = np.arange(np.shape(field)[0])
check_data(field, x, y)
xlabel = ''
ylabel = ''
# Assign irregular and orca keywords unless already set
if irregular is None:
if np.size(x) == np.size(np.unique(x)):
irregular = False
else:
irregular = True
if np.ndim(x) == 2 and np.ndim(y) == 2:
if orca is None:
orca = orca_check(x)
if orca:
# Apply orca_skip if set
if orca_skip is not None:
print('applying orca_skip value of ', orca_skip)
x = x[::orca_skip, ::orca_skip]
y = y[::orca_skip, ::orca_skip]
field = field[::orca_skip, ::orca_skip]
# orca grids have a discontinuity in the longitude grid
# use the method at https://gist.github.com/pelson/79cf31ef324774c97ae7
# to remove the discontinuity
fixed_x = x.copy()
for i, start in enumerate(np.argmax(np.abs(np.diff(x)) > 180, axis=1)):
fixed_x[i, start+1:] += 360
x = fixed_x
if np.ndim(x) == 2:
irregular = False
# Set contour line styles
matplotlib.rcParams['contour.negative_linestyle'] = negative_linestyle
# Set contour lines off on block plots
if blockfill:
fill = False
field_orig = deepcopy(field)
x_orig = deepcopy(x)
y_orig = deepcopy(y)
# Check number of colours and levels match if user has modified the
# number of colours
if plotvars.cscale_flag == 2:
ncols = np.size(plotvars.cs)
nintervals = np.size(plotvars.levels) - 1
if plotvars.levels_extend == 'min':
nintervals += 1
if plotvars.levels_extend == 'max':
nintervals += 1
if plotvars.levels_extend == 'both':
nintervals += 2
if ncols != nintervals:
errstr = "\n\ncfp.con - blockfill error \n"
errstr += "need to match number of colours and contour intervals\n"
errstr += "Don't forget to take account of the colorbar "
errstr += "extensions\n\n"
raise TypeError(errstr)
# Turn off colorbar if fill is turned off
if not fill and not blockfill and not blockfill_ugrid:
colorbar = False
# Revert to default colour scale if cscale_flag flag is set
if plotvars.cscale_flag == 0:
plotvars.cs = cscale1
# Set the orientation of the colorbar
if plotvars.plot_type == 1:
if plotvars.proj == 'npstere' or plotvars.proj == 'spstere':
if colorbar_orientation is None:
colorbar_orientation = 'vertical'
if colorbar_orientation is None:
colorbar_orientation = 'horizontal'
# Store original map resolution
resolution_orig = plotvars.resolution
# Set size of color bar if not specified
if colorbar_shrink is None:
colorbar_shrink = 1.0
if plotvars.proj == 'npstere' or plotvars.proj == 'spstere':
colorbar_shrink = 0.8
# Set plot type if user specified
if (ptype is not None):
plotvars.plot_type = ptype
# Get contour levels if none are defined
spacing = 'linear'
if plotvars.level_spacing is not None:
spacing = plotvars.level_spacing
if level_spacing is not None:
spacing = level_spacing
if plotvars.levels is None:
if isinstance(f, cf.Field):
field, x, y, ptype, colorbar_title, xlabel, ylabel, xpole, ypole =\
cf_data_assign(f, colorbar_title, verbose=verbose)
clevs, mult, fmult = calculate_levels(field=field,
level_spacing=spacing,
verbose=verbose)
else:
clevs = plotvars.levels
mult = 0
fmult = 1
# Set the colour scale if nothing is defined
includes_zero = False
if plotvars.cscale_flag == 0:
col_zero = 0
for cval in clevs:
if includes_zero is False:
col_zero = col_zero + 1
if cval == 0:
includes_zero = True
if includes_zero:
cs_below = col_zero
cs_above = np.size(clevs) - col_zero + 1
if plotvars.levels_extend == 'max' or plotvars.levels_extend == 'neither':
cs_below = cs_below - 1
if plotvars.levels_extend == 'min' or plotvars.levels_extend == 'neither':
cs_above = cs_above - 1
uniform = True
if plotvars.cs_uniform is False:
uniform = False
cscale('scale1', below=cs_below, above=cs_above, uniform=uniform)
else:
ncols = np.size(clevs)+1
if plotvars.levels_extend == 'min' or plotvars.levels_extend == 'max':
ncols = ncols-1
if plotvars.levels_extend == 'neither':
ncols = ncols-2
cscale('viridis', ncols=ncols)
plotvars.cscale_flag = 0
# User selected colour map but no mods so fit to levels
if plotvars.cscale_flag == 1:
ncols = np.size(clevs)+1
if plotvars.levels_extend == 'min' or plotvars.levels_extend == 'max':
ncols = ncols-1
if plotvars.levels_extend == 'neither':
ncols = ncols-2
cscale(plotvars.cs_user, ncols=ncols)
plotvars.cscale_flag = 1
# Set colorbar labels
# Set a sensible label spacing if the user hasn't already done so
if colorbar_label_skip is None:
if colorbar_orientation == 'horizontal':
nchars = 0
for lev in clevs:
nchars = nchars + len(str(lev))
colorbar_label_skip = int(nchars / 80 + 1)
if plotvars.columns > 1:
colorbar_label_skip = int(nchars * (plotvars.columns) / 80)
else:
colorbar_label_skip = 1
if colorbar_label_skip > 1:
if includes_zero:
# include zero in the colorbar labels
zero_pos = [i for i, mylev in enumerate(clevs) if mylev == 0][0]
cbar_labels = clevs[zero_pos]
i = zero_pos + colorbar_label_skip
while i <= len(clevs) - 1:
cbar_labels = np.append(cbar_labels, clevs[i])
i = i + colorbar_label_skip
i = zero_pos - colorbar_label_skip
if i >= 0:
while i >= 0:
cbar_labels = np.append(clevs[i], cbar_labels)
i = i - colorbar_label_skip
else:
cbar_labels = clevs[0]
i = int(colorbar_label_skip)
while i <= len(clevs) - 1:
cbar_labels = np.append(cbar_labels, clevs[i])
i = i + colorbar_label_skip
else:
cbar_labels = clevs
if colorbar_label_skip is None:
colorbar_label_skip = 1
# Make a list of strings of the colorbar levels for later labelling
clabels = []
for i in cbar_labels:
clabels.append(str(i))
if colorbar_label_skip > 1:
for skip in np.arange(colorbar_label_skip - 1):
clabels.append('')
if colorbar_labels is not None:
cbar_labels = colorbar_labels
else:
cbar_labels = clabels
# Turn off line_labels if the field is all the same
# Matplotlib 3.2.2 throws an error if there are no line labels
if np.nanmin(field) == np.nanmax(field):
line_labels = False
# Add mult to colorbar_title if used
if (colorbar_title is None):
colorbar_title = ''
if (mult != 0):
colorbar_title = colorbar_title + ' *10$^{' + str(mult) + '}$'
# Catch null title
if title is None:
title = ''
if plotvars.title is not None:
title = plotvars.title
# Set plot variables
title_fontsize = plotvars.title_fontsize
text_fontsize = plotvars.text_fontsize
if colorbar_fontsize is None:
colorbar_fontsize = plotvars.colorbar_fontsize
if colorbar_fontweight is None:
colorbar_fontweight = plotvars.colorbar_fontweight
continent_thickness = plotvars.continent_thickness
continent_color = plotvars.continent_color
continent_linestyle = plotvars.continent_linestyle
land_color = plotvars.land_color
ocean_color = plotvars.ocean_color
lake_color = plotvars.lake_color
title_fontweight = plotvars.title_fontweight
if continent_thickness is None:
continent_thickness = 1.5
if continent_color is None:
continent_color = 'k'
if continent_linestyle is None:
continent_linestyle = 'solid'
cb_orient = colorbar_orientation
# Retrieve any user defined axis labels
if xlabel == '' and plotvars.xlabel is not None:
xlabel = plotvars.xlabel
if ylabel == '' and plotvars.ylabel is not None:
ylabel = plotvars.ylabel
if xticks is None and plotvars.xticks is not None:
xticks = plotvars.xticks
if plotvars.xticklabels is not None:
xticklabels = plotvars.xticklabels
else:
xticklabels = list(map(str, xticks))
if yticks is None and plotvars.yticks is not None:
yticks = plotvars.yticks
if plotvars.yticklabels is not None:
yticklabels = plotvars.yticklabels
else:
yticklabels = list(map(str, yticks))
# Calculate a set of dimension titles if requested
if titles:
plotvars.titles_con_called = True
title_dims = generate_titles(f)
if not colorbar:
title_dims = colorbar_title + '\n' + title_dims
# Check if data is well formed
# i.e. dimensions have only recognizable X, Y, Z, T or a subset
well_formed = False
if isinstance(f, cf.Field):
well_formed = check_well_formed(f)
if nlevs is not False:
clevs = nlevs
plotvars.levels_extend = 'neither'
if plotvars.cscale_flag == 0:
if np.min(field) < 0 and np.max(field) > 0:
cscale('scale1', ncols=nlevs)
else:
cscale('viridis', ncols=nlevs)
plotvars.cscale_flag = 0
else:
cscale(plotvars.cs_user, ncols=nlevs)
##################
# Map contour plot
##################
if ptype == 1:
if verbose:
print('con - making a map plot')
# Open a new plot if necessary
if plotvars.user_plot == 0:
gopen(user_plot=0)
# Reset the stored mapping
if plotvars.user_mapset == 0:
plotvars.lonmin = -180
plotvars.lonmax = 180
plotvars.latmin = -90
plotvars.latmax = 90
# Set up mapping
mylonmin = np.nanmin(x)
mylonmax = np.nanmax(x)
mylatmin = np.nanmin(y)
mylatmax = np.nanmax(y)
lonrange = mylonmax - mylonmin
latrange = mylatmax - mylatmin
if lonrange > 360.0:
mylonmax = mylonmin + 360.0
lonrange = 360.0
if (lonrange > 350 and latrange > 160) or plotvars.user_mapset == 1:
set_map()
else:
mapset(lonmin=mylonmin, lonmax=mylonmax, latmin=mylatmin, latmax=mylatmax,
user_mapset=0, resolution=resolution_orig)
set_map()
mymap = plotvars.mymap
user_mapset = plotvars.user_mapset
lonrange = np.nanmax(x) - np.nanmin(x)
if not blockfill_ugrid and not blockfill_2d:
if not irregular:
if lonrange > 350 and np.ndim(y) == 1:
# Add cyclic information if missing.
if lonrange < 360:
# field, x = cartopy_util.add_cyclic_point(field, x)
# Call add_cyclic_point it spacing is regular
x_regular = True
xspacing = x[1] - x[0]
for ix in np.arange(len(x) - 1):
if x[ix+1] - x[ix] != xspacing:
x_regular = False
if x_regular:
field, x = add_cyclic(field, x)
lonrange = np.nanmax(x) - np.nanmin(x)
# cartopy line drawing fix
if x[-1] - x[0] == 360.0:
x[-1] = x[-1] + 0.001
# Shift grid if needed
if plotvars.lonmin < np.nanmin(x):
# Cartopy feature at version 0.20.0
# -360 to 0 creates strange contours
vers = cartopy.__version__.split('.')
val = int(vers[0] +vers[1])
if val < 20:
x = x - 360
if plotvars.lonmin > np.nanmax(x):
x = x + 360
elif not orca:
# Get the irregular data within the map coordinates
# Matplotlib tricontour cannot plot missing data so we need to split
# the missing data into a separate field to deal with this
field_modified = deepcopy(field)
pts_nan = np.where(np.isnan(field_modified))
field_modified[pts_nan] = -1e30
field_irregular, lons_irregular, lats_irregular = irregular_window(field_modified, x, y)
#pts_real = np.where(np.isfinite(field_irregular))
pts_real = np.where(field_irregular > -1e29)
pts_nan = np.where(field_irregular < -1e29)
field_irregular_nan = []
lons_irregular_nan = []
lats_irregular_nan = []
if np.size(pts_nan) > 0:
field_irregular_nan = deepcopy(field_irregular)
lons_irregular_nan = deepcopy(lons_irregular)
lats_irregular_nan = deepcopy(lats_irregular)
field_irregular_nan[:] = 0
field_irregular_nan[pts_nan] = 1
field_irregular_real = deepcopy(field_irregular[pts_real])
lons_irregular_real = deepcopy(lons_irregular[pts_real])
lats_irregular_real = deepcopy(lats_irregular[pts_real])
if not irregular:
# Flip latitudes and field if latitudes are in descending order
if np.ndim(y) == 1:
if y[0] > y[-1]:
y = y[::-1]
field = np.flipud(field)
# Plotting a sub-area of the grid produces stray contour labels
# in polar plots. Subsample the latitudes to remove this problem
if plotvars.proj == 'npstere' and np.ndim(y) == 1:
if not blockfill_ugrid and not blockfill_2d:
if irregular:
pts = np.where(lats_irregular > plotvars.boundinglat - 5)
pts = np.array(pts).flatten()
lons_irregular_real = lons_irregular_real[pts]
lats_irregular_real = lats_irregular_real[pts]
field_irregular_real = field_irregular_real[pts]
else:
myypos = find_pos_in_array(vals=y, val=plotvars.boundinglat)
if myypos != -1:
y = y[myypos:]
field = field[myypos:, :]
if plotvars.proj == 'spstere' and np.ndim(y) == 1:
if not blockfill_ugrid and not blockfill_2d:
if irregular:
pts = np.where(lats_irregular_real < plotvars.boundinglat + 5)
lons_irregular_real = lons_irregular_real[pts]
lats_irregular_real = lats_irregular_real[pts]
field_irregular_real = field_irregular_real[pts]
else:
myypos = find_pos_in_array(vals=y, val=plotvars.boundinglat, above=True)
if myypos != -1:
y = y[0:myypos + 1]
field = field[0:myypos + 1, :]
# Set the longitudes and latitudes
lons, lats = x, y
# Set the plot limits
if lonrange > 350:
gset(
xmin=plotvars.lonmin,
xmax=plotvars.lonmax,
ymin=plotvars.latmin,
ymax=plotvars.latmax,
user_gset=0)
else:
if user_mapset == 1:
gset(xmin=plotvars.lonmin,
xmax=plotvars.lonmax,
ymin=plotvars.latmin,
ymax=plotvars.latmax,
user_gset=0)
else:
gset(xmin=np.nanmin(lons),
xmax=np.nanmax(lons),
ymin=np.nanmin(lats),
ymax=np.nanmax(lats),
user_gset=0)
# Filled contours
if fill:
if verbose:
print('con - adding filled contours')
# Get colour scale for use in contouring
# If colour bar extensions are enabled then the colour map goes
# from 1 to ncols-2. The colours for the colour bar extensions
# are then changed on the colorbar and plot after the plot is made
colmap = cscale_get_map()
cmap = matplotlib.colors.ListedColormap(colmap)
if (plotvars.levels_extend ==
'min' or plotvars.levels_extend == 'both'):
cmap.set_under(plotvars.cs[0])
if (plotvars.levels_extend ==
'max' or plotvars.levels_extend == 'both'):
cmap.set_over(plotvars.cs[-1])
# For fast map contours add transform_first=True to contourf command
# and make lons and lats 2D
if transform_first is None and np.ndim(lons) == 1 and np.ndim(lats) == 1:
if np.size(lons) >= 400:
transform_first = True
# Fast map contours are also needed when clevs is a integer
if type(clevs) == int and plotvars.plot_type == 1 and plotvars.proj == 'cyl':
transform_first=True
if transform_first:
if np.ndim(lons) == 1 and np.ndim(lats) == 1:
lons, lats = np.meshgrid(lons, lats)
# Filled colour contours
if not irregular or orca is True:
plotvars.image = mymap.contourf(lons, lats, field * fmult, clevs,
extend=plotvars.levels_extend,
cmap=cmap, norm=plotvars.norm,
alpha=alpha, transform=ccrs.PlateCarree(),
zorder=zorder, transform_first=transform_first)
else:
if np.size(field_irregular_real) > 0:
plotvars.image = mymap.tricontourf(lons_irregular_real, lats_irregular_real, field_irregular_real * fmult,
clevs, extend=plotvars.levels_extend,
cmap=cmap, norm=plotvars.norm,
alpha=alpha, transform=ccrs.PlateCarree(),
zorder=zorder)
# Block fill
if blockfill and not blockfill_ugrid:
if verbose:
print('con - adding blockfill')
two_d = False
if np.ndim(x) == 2 and np.ndim(y) == 2:
two_d = True
if isinstance(f, cf.Field):
if f.ref('grid_mapping_name:transverse_mercator', default=False):
# Special case for transverse mercator
bfill(f=f, clevs=clevs, lonlat=False, alpha=alpha, fast=blockfill_fast, zorder=zorder)
#elif orca:
elif two_d:
#bfill(f=f, clevs=clevs, lonlat=False, alpha=alpha, fast=blockfill_fast,zorder=zorder)
#bfill(x=x, y=y, f=field * fmult, clevs=clevs, lonlat=False, alpha=alpha,\
# fast=blockfill_fast, zorder=zorder, orca=True)
bfill(x=x, y=y, f=field * fmult, clevs=clevs, lonlat=False, alpha=alpha,\
fast=blockfill_fast, zorder=zorder)
else:
if f.coord('X').has_bounds() and f.coord('Y').has_bounds():
xpts = np.squeeze(f.coord('X').bounds.array[:, 0])
ypts = np.squeeze(f.coord('Y').bounds.array[:, 0])
# Add last longitude point
xpts = np.append(xpts, f.coord('X').bounds.array[-1, 1])
# Add last latitude point
ypts = np.append(ypts, f.coord('Y').bounds.array[-1, 1])
bfill(f=field_orig * fmult, x=xpts, y=ypts, clevs=clevs,
lonlat=True, bound=1, alpha=alpha, fast=blockfill_fast, zorder=zorder)
else:
bfill(f=field_orig * fmult, x=x_orig, y=y_orig, clevs=clevs,
lonlat=True, bound=0, alpha=alpha, fast=blockfill_fast, zorder=zorder)
else:
bfill(f=field_orig * fmult, x=x_orig, y=y_orig, clevs=clevs,
lonlat=True, bound=0, alpha=alpha, fast=blockfill_fast, zorder=zorder)
# Block fill for irregular
if blockfill_ugrid and not blockfill_2d:
if verbose:
print('con - adding blockfill for irregular')
bfill_ugrid(f=field_orig * fmult, face_lons=face_lons_array,
face_lats=face_lats_array,
face_connectivity=face_connectivity_array, clevs=clevs,
alpha=alpha, zorder=zorder)
# Contour lines and labels
if lines:
if verbose:
print('con - adding contour lines and labels')
if not irregular or blockfill_2d or orca:
cs = mymap.contour(lons, lats, field * fmult, clevs, colors=colors,
linewidths=linewidths, linestyles=linestyles, alpha=alpha,
transform=ccrs.PlateCarree(), zorder=zorder)
else:
cs = mymap.tricontour(lons_irregular_real, lats_irregular_real, field_irregular_real * fmult,
clevs, colors=colors,
linewidths=linewidths, linestyles=linestyles, alpha=alpha,
transform=ccrs.PlateCarree(), zorder=zorder)
if line_labels and type(clevs) != int:
nd = ndecs(clevs)
fmt = '%d'
if nd != 0:
fmt = '%1.' + str(nd) + 'f'
plotvars.plot.clabel(cs, levels=clevs, fmt=fmt, zorder=zorder, colors=colors,
fontsize=text_fontsize)
# Thick zero contour line
if zero_thick:
cs = mymap.contour(lons, lats, field * fmult, [-1e-32, 0],
colors=colors, linewidths=zero_thick,
linestyles=linestyles, alpha=alpha,
transform=ccrs.PlateCarree(), zorder=zorder)
# Add a irregular mask if there is one
if irregular and not blockfill_ugrid and not orca and not blockfill_2d:
if np.size(field_irregular_nan) > 0:
cmap_white = matplotlib.colors.ListedColormap([1.0, 1.0, 1.0])
mymap.tricontourf(lons_irregular_nan, lats_irregular_nan, field_irregular_nan , [0.5, 1.5],
extend='neither',
cmap=cmap_white, norm=plotvars.norm,
alpha=alpha, transform=ccrs.PlateCarree(),
zorder=zorder)
# Axes
plot_map_axes(axes=axes, xaxis=xaxis, yaxis=yaxis,
xticks=xticks, xticklabels=xticklabels,
yticks=yticks, yticklabels=yticklabels,
user_xlabel=user_xlabel, user_ylabel=user_ylabel,
verbose=verbose)
# Coastlines and features
feature = cfeature.NaturalEarthFeature(name='land',
category='physical',
scale=plotvars.resolution,
facecolor='none')
mymap.add_feature(feature,
edgecolor=continent_color,
linewidth=continent_thickness,
linestyle=continent_linestyle,
zorder=zorder)
if ocean_color is not None:
mymap.add_feature(cfeature.OCEAN, edgecolor='face', facecolor=ocean_color,
zorder=plotvars.feature_zorder)
if land_color is not None:
mymap.add_feature(cfeature.LAND, edgecolor='face', facecolor=land_color,
zorder=plotvars.feature_zorder)
if lake_color is not None:
mymap.add_feature(cfeature.LAKES, edgecolor='face', facecolor=lake_color,
zorder=plotvars.feature_zorder)
if grid:
map_grid()
# Title
if title != '':
map_title(title)
# Titles for dimensions
if titles:
dim_titles(title=title_dims)
# Color bar
if colorbar:
cbar(labels=cbar_labels, orientation=cb_orient, position=colorbar_position,
shrink=colorbar_shrink, title=colorbar_title, fontsize=colorbar_fontsize,
fontweight=colorbar_fontweight, text_up_down=colorbar_text_up_down,
text_down_up=colorbar_text_down_up, drawedges=colorbar_drawedges,
fraction=colorbar_fraction, thick=colorbar_thick,
anchor=colorbar_anchor, levs=clevs, verbose=verbose)
# Reset plot limits if not a user plot
if plotvars.user_gset == 0:
gset()
################################################
# Latitude, longitude or time vs Z contour plots
################################################
if ptype == 2 or ptype == 3 or ptype == 7:
if verbose:
if ptype == 2:
print('con - making a latitude-pressure plot')
if ptype == 3:
print('con - making a longitude-pressure plot')
if ptype == 7:
print('con - making a time-pressure plot')
# Work out which way is up
positive = None
myz = find_z(f)
if isinstance(f, cf.Field) and well_formed:
if hasattr(f.construct(myz), 'positive'):
positive = f.construct(myz).positive
else:
errstr = "\ncf-plot - data error \n"
errstr += "data needs a vertical coordinate direction"
errstr += " as required in CF data conventions"
errstr += "\nMaking a contour plot assuming positive is down\n\n"
errstr += "If this is incorrect the data needs to be modified to \n"
errstr += "include a correct value for the direction attribute\n"
errstr += "such as in f.coord(\'Z\').positive=\'down\'"
errstr += "\n\n"
print(errstr)
positive = 'down'
else:
positive = 'down'
if 'theta' in ylabel.split(' '):
positive = 'up'
if 'height' in ylabel.split(' '):
positive = 'up'
if plotvars.user_plot == 0:
gopen(user_plot=0)
# Use gset parameter of ylog if user has set this
if plotvars.ylog is True or plotvars.ylog == 1:
ylog = True
# Set plot limits
user_gset = plotvars.user_gset
if user_gset == 0:
# Program selected data plot limits
xmin = np.nanmin(x)
if xmin < -80 and xmin >= -90:
xmin = -90
xmax = np.nanmax(x)
if xmax > 80 and xmax <= 90:
xmax = 90
if positive == 'down':
ymin = np.nanmax(y)
ymax = np.nanmin(y)
if ymax < 10:
ymax = 0
else:
ymin = np.nanmin(y)
ymax = np.nanmax(y)
else:
# Use user specified plot limits
xmin = plotvars.xmin
xmax = plotvars.xmax
ymin = plotvars.ymin
ymax = plotvars.ymax
ystep = 100
myrange = abs(ymax - ymin)
if myrange < 1:
ystep = abs(ymax - ymin)/10.
if abs(ymax - ymin) > 1:
ystep = 1
if abs(ymax - ymin) > 10:
ystep = 10
if abs(ymax - ymin) > 100:
ystep = 100
if abs(ymax - ymin) > 1000:
ystep = 200
if abs(ymax - ymin) > 2000:
ystep = 500
if abs(ymax - ymin) > 5000:
ystep = 1000
if abs(ymax - ymin) > 15000:
ystep = 5000
# Work out ticks and tick labels
if ylog is False or ylog == 0:
heightticks = gvals(dmin=min(ymin, ymax),
dmax=max(ymin, ymax),
mystep=ystep, mod=False)[0]
if myrange < 1 and myrange > 0.1:
heightticks = np.arange(10)/10.0
else:
heightticks = []
for tick in 1000, 100, 10, 1:
if tick >= min(ymin, ymax) and tick <= max(ymin, ymax):
heightticks.append(tick)
heightlabels = heightticks
if axes:
if xaxis:
if xticks is not None:
if xticklabels is None:
xticklabels = xticks
else:
xticks = [100000000]
xticklabels = xticks
xlabel = ''
if yaxis:
if yticks is not None:
heightticks = yticks
heightlabels = yticks
if yticklabels is not None:
heightlabels = yticklabels
else:
heightticks = [100000000]
ylabel = ''
else:
xticks = [100000000]
xticklabels = xticks
heightticks = [100000000]
heightlabels = heightticks
xlabel = ''
ylabel = ''
if yticks is None:
yticks = heightticks
yticklabels = heightlabels
# Time - height contour plot
if ptype == 7:
if isinstance(f, cf.Field):
if plotvars.user_gset == 0:
tmin = f.construct('T').dtarray[0]
tmax = f.construct('T').dtarray[-1]
else:
# Use user set values if present
tmin = plotvars.xmin
tmax = plotvars.xmax
ref_time = f.construct('T').units
ref_calendar = f.construct('T').calendar
time_units = cf.Units(ref_time, ref_calendar)
t = cf.Data(cf.dt(tmin), units=time_units)
xmin = t.array
t = cf.Data(cf.dt(tmax), units=time_units)
xmax = t.array
if xticks is None and xaxis:
if ptype == 2:
xticks, xticklabels = mapaxis(min=xmin, max=xmax, type=2) # lat-pressure
if ptype == 3:
xticks, xticklabels = mapaxis(min=xmin, max=xmax, type=1) # lon-pressure
if ptype == 7:
# time-pressure
if isinstance(f, cf.Field):
# Change plotvars.xmin and plotvars.xmax from a date string
# to a number
ref_time = f.construct('T').units
ref_calendar = f.construct('T').calendar
time_units = cf.Units(ref_time, ref_calendar)
t = cf.Data(cf.dt(tmin), units=time_units)
xmin = t.array
t = cf.Data(cf.dt(tmax), units=time_units)
xmax = t.array
taxis = cf.Data(
[cf.dt(tmin), cf.dt(tmax)], units=time_units)
time_ticks, time_labels, tlabel = timeaxis(taxis)
# Use user supplied labels if present
if user_xlabel is None:
xlabel = tlabel
if xticks is None:
xticks = time_ticks
if xticklabels is None:
xticklabels = time_labels
else:
errstr = '\nNot a CF field\nPlease use ptype=0 and '
errstr = errstr + 'specify axis labels manually\n'
raise Warning(errstr)
# Set plot limits
if ylog is False or ylog == 0:
gset(xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax,
user_gset=user_gset)
else:
if ymax == 0:
ymax = 1 # Avoid zero in a log plot
gset(xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax,
ylog=True, user_gset=user_gset)
# Label axes
axes_plot(xticks=xticks, xticklabels=xticklabels, yticks=heightticks,
yticklabels=heightlabels, xlabel=xlabel, ylabel=ylabel)
# Get colour scale for use in contouring
# If colour bar extensions are enabled then the colour map goes
# from 1 to ncols-2. The colours for the colour bar extensions are
# then changed on the colorbar and plot after the plot is made
colmap = cscale_get_map()
# Filled contours
if fill:
colmap = cscale_get_map()
cmap = matplotlib.colors.ListedColormap(colmap)
if (plotvars.levels_extend ==
'min' or plotvars.levels_extend == 'both'):
cmap.set_under(plotvars.cs[0])
if (plotvars.levels_extend ==
'max' or plotvars.levels_extend == 'both'):
cmap.set_over(plotvars.cs[-1])
plotvars.image = plotvars.plot.contourf(x, y, field * fmult, clevs,
extend=plotvars.levels_extend,
cmap=cmap,
norm=plotvars.norm, alpha=alpha,
zorder=zorder)
# Block fill
if blockfill:
if isinstance(f, cf.Field):
hasbounds = True
if ptype == 2:
if f.coord('Y').has_bounds() and f.coord('Z').has_bounds():
xpts = np.squeeze(f.coord('Y').bounds.array)[:, 0]
xpts = np.append(xpts, f.coord('Y').bounds.array[-1, 1])
ypts = np.squeeze(f.coord('Z').bounds.array)[:, 0]
ypts = np.append(ypts, f.coord('Z').bounds.array[-1, 1])
else:
hasbounds = False
if ptype == 3:
if f.coord('X').has_bounds() and f.coord('Z').has_bounds():
xpts = np.squeeze(f.coord('X').bounds.array)[:, 0]
xpts = np.append(xpts, f.coord('X').bounds.array[-1, 1])
ypts = np.squeeAllTrop_UpStrat_Eq_Total_AllWN_Timeseries_2ze(f.coord('Z').bounds.array)[:, 0]
ypts = np.append(xpts, f.coord('Z').bounds.array[-1, 1])
else:
hasbounds = False
if ptype == 7:
if f.coord('T').has_bounds() and f.coord('Z').has_bounds():
xpts = np.squeeze(f.coord('T').bounds.array)[:, 0]
xpts = np.append(xpts, f.coord('T').bounds.array[-1, 1])
ypts = np.squeeze(f.coord('Z').bounds.array)[:, 0]
ypts = np.append(xpts, f.coord('Z').bounds.array[-1, 1])
else:
hasbounds = False
if hasbounds:
bfill(f=field_orig * fmult, x=xpts, y=ypts, clevs=clevs,
lonlat=False, bound=1, alpha=alpha, fast=blockfill_fast, zorder=zorder)
else:
bfill(f=field_orig * fmult, x=x_orig, y=y_orig, clevs=clevs,
lonlat=False, bound=0, alpha=alpha, fast=blockfill_fast, zorder=zorder)
else:
bfill(f=field_orig * fmult, x=x_orig, y=y_orig, clevs=clevs,
lonlat=False, bound=0, alpha=alpha, fast=blockfill_fast, zorder=zorder)
# Contour lines and labels
if lines:
cs = plotvars.plot.contour(
x, y, field * fmult, clevs, colors=colors,
linewidths=linewidths, linestyles=linestyles, zorder=zorder)
if line_labels and type(clevs) != int:
nd = ndecs(clevs)
fmt = '%d'
if nd != 0:
fmt = '%1.' + str(nd) + 'f'
plotvars.plot.clabel(cs,fmt=fmt,colors=colors, zorder=zorder,
fontsize=text_fontsize)
# Thick zero contour line
if zero_thick:
cs = plotvars.plot.contour(x, y, field * fmult,
[-1e-32, 0], colors=colors,
linewidths=zero_thick,
linestyles=linestyles, alpha=alpha,
zorder=zorder)
# Titles for dimensions
if titles:
dim_titles(title=title_dims)
# Color bar
if colorbar:
cbar(labels=cbar_labels,
orientation=cb_orient,
position=colorbar_position,
shrink=colorbar_shrink,
title=colorbar_title,
fontsize=colorbar_fontsize,
fontweight=colorbar_fontweight,
text_up_down=colorbar_text_up_down,
text_down_up=colorbar_text_down_up,
drawedges=colorbar_drawedges,
fraction=colorbar_fraction,
thick=colorbar_thick,
levs=clevs,
anchor=colorbar_anchor,
verbose=verbose)
# Title
plotvars.plot.set_title(title, y=1.03, fontsize=title_fontsize,
fontweight=title_fontweight)
# Reset plot limits to those supplied by the user
if user_gset == 1 and ptype == 7:
gset(xmin=tmin, xmax=tmax, ymin=ymin, ymax=ymax,
user_gset=user_gset)
# reset plot limits if not a user plot
if plotvars.user_gset == 0:
gset()
########################
# Hovmuller contour plot
########################
if (ptype == 4 or ptype == 5):
if verbose:
print('con - making a Hovmuller plot')
yplotlabel = 'Time'
if ptype == 4:
xplotlabel = 'Longitude'
if ptype == 5:
xplotlabel = 'Latitude'
user_gset = plotvars.user_gset
# Time strings set to None initially
tmin = None
tmax = None
# Set plot limits
if all(val is not None for val in [
plotvars.xmin, plotvars.xmax, plotvars.ymin, plotvars.ymax]):
# Store time strings for later use
tmin = plotvars.ymin
tmax = plotvars.ymax
# Check data has CF attributes needed
check_units = check_units = True
check_calendar = True
check_Units_reftime = True
if hasattr(f.construct('T'), 'units') is False:
check_units = False
if hasattr(f.construct('T'), 'calendar') is False:
check_calendar = False
if hasattr(f.construct('T'), 'Units'):
if not hasattr(f.construct('T').Units, 'reftime'):
check_Units_reftime = False
else:
check_Units_reftime = False
if False in [check_units, check_calendar, check_Units_reftime]:
print('\nThe required CF time information to make the plot')
print('is not available please fix the following before')
print('trying to plot again')
if check_units is False:
print('Time axis missing: units')
if check_calendar is False:
print('Time axis missing: calendar')
if check_Units_reftime is False:
print('Time axis missing: Units.reftime')
return
# Change from date string in ymin and ymax to date as a float
ref_time = f.construct('T').units
ref_calendar = f.construct('T').calendar
time_units = cf.Units(ref_time, ref_calendar)
t = cf.Data(cf.dt(plotvars.ymin), units=time_units)
ymin = t.array
t = cf.Data(cf.dt(plotvars.ymax), units=time_units)
ymax = t.array
xmin = plotvars.xmin
xmax = plotvars.xmax
else:
xmin = np.nanmin(x)
xmax = np.nanmax(x)
ymin = np.nanmin(y)
ymax = np.nanmax(y)
# Extract axis labels
if len(f.constructs('T')) > 1:
errstr = "\n\nTime axis error - only one time axis allowed\n "
errstr += "Please list time axes with print(f.constructs())\n"
errstr += "and remove the ones not needed for a hovmuller plot \n"
errstr += "with f.del_construct('unwanted_time_axis')\n"
errstr += "before trying to plot again\n\n\n\n"
raise TypeError(errstr)
time_ticks, time_labels, ylabel = timeaxis(f.construct('T'))
if ptype == 4:
lonlatticks, lonlatlabels = mapaxis(min=xmin, max=xmax, type=1)
if ptype == 5:
lonlatticks, lonlatlabels = mapaxis(min=xmin, max=xmax, type=2)
if axes:
if xaxis:
if xticks is not None:
lonlatticks = xticks
lonlatlabels = xticks
if xticklabels is not None:
lonlatlabels = xticklabels
else:
lonlatticks = [100000000]
xlabel = ''
if yaxis:
if yticks is not None:
timeticks = yticks
timelabels = yticks
if yticklabels is not None:
timelabels = yticklabels
else:
timeticks = [100000000]
ylabel = ''
else:
timeticks = [100000000]
xplotlabel = ''
yplotlabel = ''
if user_xlabel is not None:
xplotlabel = user_xlabel
if user_ylabel is not None:
yplotlabel = user_ylabel
# Use the automatically generated labels if none are supplied
if ylabel is None:
yplotlabel = 'time'
if np.size(time_ticks) > 0:
timeticks = time_ticks
if np.size(time_labels) > 0:
timelabels = time_labels
# Swap axes if requested
if swap_axes:
x, y = y, x
field = np.flipud(np.rot90(field))
xmin, ymin = ymin, xmin
xmax, ymax = ymax, xmax
xplotlabel, yplotlabel = yplotlabel, xplotlabel
lonlatticks, timeticks = timeticks, lonlatticks
lonlatlabels, timelabels = timelabels, lonlatlabels
# Set plot limits
if plotvars.user_plot == 0:
gopen(user_plot=0)
gset(xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, user_gset=user_gset)
# Revert to time strings if set
if all(val is not None for val in [tmin, tmax]):
plotvars.ymin = tmin
plotvars.ymax = tmax
# Set and label axes
axes_plot(xticks=lonlatticks, xticklabels=lonlatlabels,
yticks=timeticks, yticklabels=timelabels,
xlabel=xplotlabel, ylabel=yplotlabel)
# Get colour scale for use in contouring
# If colour bar extensions are enabled then the colour map goes
# from 1 to ncols-2. The colours for the colour bar extensions are
# then changed on the colorbar and plot after the plot is made
colmap = cscale_get_map()
# Filled contours
if fill:
colmap = cscale_get_map()
cmap = matplotlib.colors.ListedColormap(colmap)
if (plotvars.levels_extend ==
'min' or plotvars.levels_extend == 'both'):
cmap.set_under(plotvars.cs[0])
if (plotvars.levels_extend ==
'max' or plotvars.levels_extend == 'both'):
cmap.set_over(plotvars.cs[-1])
plotvars.image = plotvars.plot.contourf(x, y, field * fmult, clevs,
extend=plotvars.levels_extend,
cmap=cmap,
norm=plotvars.norm, alpha=alpha,
zorder=zorder)
# Block fill
if blockfill:
if isinstance(f, cf.Field):
if f.coord('X').has_bounds():
if ptype == 4:
xpts = np.squeeze(f.coord('X').bounds.array)[:, 0]
xpts = np.append(xpts, f.coord('X').bounds.array[-1, 1])
if ptype == 5:
xpts = np.squeeze(f.coord('Y').bounds.array)[:, 0]
xpts = np.append(xpts, f.coord('Y').bounds.array[-1, 1])
ypts = np.squeeze(f.coord('T').bounds.array)[:, 0]
ypts = np.append(ypts, f.coord('T').bounds.array[-1, 1])
if swap_axes:
xpts, ypts = ypts, xpts
field_orig = np.flipud(np.rot90(field_orig))
bfill(f=field_orig * fmult, x=xpts, y=ypts, clevs=clevs,
lonlat=False, bound=1, alpha=alpha, fast=blockfill_fast, zorder=zorder)
else:
if swap_axes:
x_orig, y_orig = y_orig, x_orig
field_orig = np.flipud(np.rot90(field_orig))
bfill(f=field_orig * fmult, x=x_orig, y=y_orig, clevs=clevs,
lonlat=False, bound=0, alpha=alpha, fast=blockfill_fast, zorder=zorder)
else:
if swap_axes:
x_orig, y_orig = y_orig, x_orig
field_orig = np.flipud(np.rot90(field_orig))
bfill(f=field_orig * fmult, x=x_orig, y=y_orig, clevs=clevs,
lonlat=False, bound=0, alpha=alpha, fast=blockfill_fast, zorder=zorder)
# Contour lines and labels
if lines:
cs = plotvars.plot.contour(x, y, field * fmult, clevs, colors=colors,
linewidths=linewidths, linestyles=linestyles, alpha=alpha)
if line_labels and type(clevs) != int:
nd = ndecs(clevs)
fmt = '%d'
if nd != 0:
fmt = '%1.' + str(nd) + 'f'
plotvars.plot.clabel(cs, fmt=fmt, colors=colors, zorder=zorder,
fontsize=text_fontsize)
# Thick zero contour line
if zero_thick:
cs = plotvars.plot.contour(x, y, field * fmult,
[-1e-32, 0], colors=colors,
linewidths=zero_thick,
linestyles=linestyles, alpha=alpha,
zorder=zorder)
# Titles for dimensions
if titles:
dim_titles(title=title_dims)
# Color bar
if colorbar:
cbar(labels=cbar_labels,
orientation=cb_orient,
position=colorbar_position,
shrink=colorbar_shrink,
title=colorbar_title,
fontsize=colorbar_fontsize,
fontweight=colorbar_fontweight,
text_up_down=colorbar_text_up_down,
text_down_up=colorbar_text_down_up,
drawedges=colorbar_drawedges,
fraction=colorbar_fraction,
thick=colorbar_thick,
levs=clevs,
anchor=colorbar_anchor,
verbose=verbose)
# Title
plotvars.plot.set_title(
title,
y=1.03,
fontsize=title_fontsize,
fontweight=title_fontweight)
# reset plot limits if not a user plot
if user_gset == 0:
gset()
###########################
# Rotated pole contour plot
###########################
if ptype == 6:
# Extract x and y grid points
if plotvars.proj == 'cyl':
xpts = x
ypts = y
else:
xpts = np.arange(np.size(x))
ypts = np.arange(np.size(y))
if verbose:
print('con - making a rotated pole plot')
user_gset = plotvars.user_gset
if plotvars.user_plot == 0:
gopen(user_plot=0)
# Set plot limits
if plotvars.proj == 'rotated':
plotargs = {}
gset(xmin=0, xmax=np.size(xpts) - 1,
ymin=0, ymax=np.size(ypts) - 1,
user_gset=user_gset)
plot = plotvars.plot
# Set plot limits
if plotvars.proj == 'UKCP':
plot = plotvars.plot
plotargs = {}
if plotvars.proj == 'cyl':
rotated_pole = f.ref('grid_mapping_name:rotated_latitude_longitude')
xpole = rotated_pole['grid_north_pole_longitude']
ypole = rotated_pole['grid_north_pole_latitude']
transform = ccrs.RotatedPole(pole_latitude=ypole,
pole_longitude=xpole)
plotargs = {'transform': transform}
if plotvars.user_mapset == 1:
set_map()
else:
if np.ndim(xpts) == 1:
lonpts, latpts = np.meshgrid(xpts, ypts)
else:
lonpts = xpts
latpts = ypts
points = ccrs.PlateCarree().transform_points(transform, lonpts.flatten(),
latpts.flatten())
lons = np.array(points)[:, 0]
lats = np.array(points)[:, 1]
mapset(lonmin=np.min(lons), lonmax=np.max(lons),
latmin=np.min(lats), latmax=np.max(lats),
user_mapset=0, resolution=resolution_orig)
set_map()
plotargs = {'transform': transform}
plot = plotvars.mymap
# Get colour scale for use in contouring
# If colour bar extensions are enabled then the colour map goes
# from 1 to ncols-2. The colours for the colour bar extensions are
# then changed on the colorbar and plot after the plot is made
colmap = cscale_get_map()
# Filled contours
if fill:
colmap = cscale_get_map()
cmap = matplotlib.colors.ListedColormap(colmap)
if (plotvars.levels_extend ==
'min' or plotvars.levels_extend == 'both'):
cmap.set_under(plotvars.cs[0])
if (plotvars.levels_extend ==
'max' or plotvars.levels_extend == 'both'):
cmap.set_over(plotvars.cs[-1])
plot.contourf(xpts, ypts, field * fmult, clevs,
extend=plotvars.levels_extend,
cmap=cmap,
norm=plotvars.norm, alpha=alpha,
zorder=zorder, **plotargs)
# Block fill
if blockfill:
bfill(f=field_orig * fmult,
x=xpts,
y=ypts,
clevs=clevs,
lonlat=False,
bound=0,
alpha=alpha, fast=blockfill_fast,
zorder=zorder, transform=transform)
# Contour lines and labels
if lines:
cs = plot.contour(xpts, ypts, field * fmult, clevs, colors=colors,
linewidths=linewidths, linestyles=linestyles,
zorder=zorder, **plotargs)
if line_labels and type(clevs) != int:
nd = ndecs(clevs)
fmt = '%d'
if nd != 0:
fmt = '%1.' + str(nd) + 'f'
plot.clabel(cs, fmt=fmt, colors=colors, zorder=zorder,
fontsize=text_fontsize)
# Thick zero contour line
if zero_thick:
cs = plot.contour(xpts, ypts, field * fmult,
[-1e-32, 0], colors=colors,
linewidths=zero_thick,
linestyles=linestyles, alpha=alpha,
zorder=zorder, **plotargs)
# Titles for dimensions
if titles:
dim_titles(title=title_dims)
# Color bar
if colorbar:
cbar(labels=cbar_labels,
orientation=cb_orient,
position=colorbar_position,
shrink=colorbar_shrink,
title=colorbar_title,
fontsize=colorbar_fontsize,
fontweight=colorbar_fontweight,
text_up_down=colorbar_text_up_down,
text_down_up=colorbar_text_down_up,
drawedges=colorbar_drawedges,
fraction=colorbar_fraction,
thick=colorbar_thick,
levs=clevs,
anchor=colorbar_anchor,
verbose=verbose)
# Rotated grid axes
if axes:
if plotvars.proj == 'cyl':
plot_map_axes(axes=axes, xaxis=xaxis, yaxis=yaxis,
xticks=xticks, xticklabels=xticklabels,
yticks=yticks, yticklabels=yticklabels,
user_xlabel=user_xlabel, user_ylabel=user_ylabel,
verbose=verbose)
else:
rgaxes(xpole=xpole, ypole=ypole, xvec=x, yvec=y,
xticks=xticks, xticklabels=xticklabels,
yticks=yticks, yticklabels=yticklabels,
axes=axes, xaxis=xaxis, yaxis=yaxis,
xlabel=xlabel, ylabel=ylabel)
if plotvars.proj == 'rotated' or plotvars.proj == 'UKCP':
# Remove Matplotlib default axis labels
axes_plot(xticks=[100000000], xticklabels=[''],
yticks=[100000000], yticklabels=[''],
xlabel='', ylabel='')
# Add title and coastlines for cylindrical projection
if plotvars.proj == 'cyl':
# Coastlines
feature = cfeature.NaturalEarthFeature(
name='land', category='physical',
scale=plotvars.resolution,
facecolor='none')
plotvars.mymap.add_feature(feature, edgecolor=continent_color,
linewidth=continent_thickness,
linestyle=continent_linestyle,
zorder=zorder)
# Title
if title != '':
map_title(title)
# Add title for native grid
if plotvars.proj == 'rotated':
# Title
plotvars.plot.set_title(title, y=1.03,
fontsize=title_fontsize,
fontweight=title_fontweight)
# reset plot limits if not a user plot
if plotvars.user_gset == 0:
gset()
#############
# Other plots
#############
if ptype == 0:
if verbose:
print('con - making an other plot')
if plotvars.user_plot == 0:
gopen(user_plot=0)
user_gset = plotvars.user_gset
# Set axis labels to None
xplotlabel = None
yplotlabel = None
cf_field = False
if f is not None:
if isinstance(f, cf.Field):
cf_field = True
f = f.squeeze()
# Work out axes if none are supplied
if any(val is None for val in [
plotvars.xmin, plotvars.xmax, plotvars.ymin, plotvars.ymax]):
xmin = np.nanmin(x)
xmax = np.nanmax(x)
ymin = np.nanmin(y)
ymax = np.nanmax(y)
else:
xmin = plotvars.xmin
xmax = plotvars.xmax
ymin = plotvars.ymin
ymax = plotvars.ymax
# Change from date string to a number if strings are passed
time_xstr = False
time_ystr = False
try:
float(xmin)
except Exception:
time_xstr = True
try:
float(ymin)
except Exception:
time_ystr = True
xaxisticks = None
yaxisticks = None
xtimeaxis = False
ytimeaxis = False
if cf_field and f.has_construct('T'):
if np.size(f.construct('T').array) > 1:
taxis = f.construct('T')
data_axes = f.get_data_axes()
count = 1
for d in data_axes:
i = f.constructs.domain_axis_identity(d)
try:
c = f.coordinate([i])
if np.size(c.array) > 1:
test_for_time_axis = False
sn = getattr(c, 'standard_name', 'NoName')
an = c.get_property('axis', 'NoName')
if (sn == 'time' or an == 'T'):
test_for_time_axis = True
if count == 1:
if test_for_time_axis:
ytimeaxis = True
elif count == 2:
if test_for_time_axis:
xtimeaxis = True
count += 1
except ValueError:
print("no sensible coordinates for this axis")
if time_xstr or time_ystr:
ref_time = f.construct('T').units
ref_calendar = f.construct('T').calendar
time_units = cf.Units(ref_time, ref_calendar)
if time_xstr:
t = cf.Data(cf.dt(xmin), units=time_units)
xmin = t.array
t = cf.Data(cf.dt(xmax), units=time_units)
xmax = t.array
taxis = cf.Data([xmin, xmax], units=time_units)
taxis.calendar = ref_calendar
if time_ystr:
t = cf.Data(cf.dt(ymin), units=time_units)
ymin = t.array
t = cf.Data(cf.dt(ymax), units=time_units)
ymax = t.array
taxis = cf.Data([ymin, ymax], units=time_units)
taxis.calendar = ref_calendar
if xtimeaxis:
xaxisticks, xaxislabels, xplotlabel = timeaxis(taxis)
if ytimeaxis:
yaxisticks, yaxislabels, yplotlabel = timeaxis(taxis)
if cf_field:
coords = list(f.coords())
mycoords = []
for coord in coords:
if np.size(f.coord(coord).array) > 1:
mycoords.append(coord)
mycoords.reverse()
for icoord in np.arange(len(mycoords)):
myaxisticks = None
myaxislabels = None
mylabel = None
if f.coord(mycoords[icoord]).X:
myaxisticks, myaxislabels = mapaxis(np.min(f.coord('X').array), np.max(f.coord('X').array), type=1)
mylabel = 'longitude'
if f.coord(mycoords[icoord]).Y:
myaxisticks, myaxislabels = mapaxis(np.min(f.coord('Y').array), np.max(f.coord('Y').array), type=2)
mylabel = 'latitude'
if myaxisticks is not None:
if icoord == 0:
xaxisticks, xaxislabels, xlabel = myaxisticks, myaxislabels, mylabel
if icoord == 1:
yaxisticks, yaxislabels, ylabel = myaxisticks, myaxislabels, mylabel
if xaxisticks is None:
xaxisticks = gvals(dmin=xmin, dmax=xmax, mod=False)[0]
xaxislabels = xaxisticks
if yaxisticks is None:
yaxisticks = gvals(dmin=ymax, dmax=ymin, mod=False)[0]
yaxislabels = yaxisticks
if user_xlabel is not None:
xplotlabel = user_xlabel
else:
if xplotlabel is None:
xplotlabel = xlabel
if user_ylabel is not None:
yplotlabel = user_ylabel
else:
if yplotlabel is None:
yplotlabel = ylabel
# Draw axes
if axes:
if xaxis:
if xticks is not None:
xaxisticks = xticks
xaxislabels = xticks
if xticklabels is not None:
xaxislabels = xticklabels
else:
xaxisticks = [100000000]
xlabel = ''
if yaxis:
if yticks is not None:
yaxisticks = yticks
yaxislabels = yticks
if yticklabels is not None:
yaxislabels = yticklabels
else:
yaxisticks = [100000000]
ylabel = ''
else:
xaxisticks = [100000000]
yaxisticks = [100000000]
xlabel = ''
ylabel = ''
# Swap axes if requested
if swap_axes:
x, y = y, x
field = np.flipud(np.rot90(field))
xmin, ymin = ymin, xmin
xmax, ymax = ymax, xmax
xplotlabel, yplotlabel = yplotlabel, xplotlabel
xaxisticks, yaxisticks = yaxisticks, xaxisticks
xaxislabels, yaxislabels = yaxislabels, xaxislabels
# Set plot limits and set default plot labels
gset(xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, user_gset=user_gset)
# Draw axes
axes_plot(xticks=xaxisticks, xticklabels=xaxislabels,
yticks=yaxisticks, yticklabels=yaxislabels,
xlabel=xplotlabel, ylabel=yplotlabel)
# Get colour scale for use in contouring
# If colour bar extensions are enabled then the colour map goes
# then from 1 to ncols-2. The colours for the colour bar extensions
# are changed on the colorbar and plot after the plot is made
colmap = cscale_get_map()
# Filled contours
if fill:
colmap = cscale_get_map()
cmap = matplotlib.colors.ListedColormap(colmap)
if (plotvars.levels_extend ==
'min' or plotvars.levels_extend == 'both'):
cmap.set_under(plotvars.cs[0])
if (plotvars.levels_extend ==
'max' or plotvars.levels_extend == 'both'):
cmap.set_over(plotvars.cs[-1])
plotvars.image = plotvars.plot.contourf(x, y, field * fmult, clevs,
extend=plotvars.levels_extend,
cmap=cmap,
norm=plotvars.norm, alpha=alpha,
zorder=zorder)
# Block fill
if blockfill:
bfill(f=field_orig * fmult, x=x_orig, y=y_orig, clevs=clevs,
lonlat=False, bound=0, alpha=alpha, fast=blockfill_fast, zorder=zorder)
# Contour lines and labels
if lines:
cs = plotvars.plot.contour(x, y, field * fmult, clevs, colors=colors,
linewidths=linewidths, linestyles=linestyles,
zorder=zorder)
if line_labels and type(clevs) != int:
nd = ndecs(clevs)
fmt = '%d'
if nd != 0:
fmt = '%1.' + str(nd) + 'f'
plotvars.plot.clabel(cs, fmt=fmt, colors=colors, zorder=zorder,
fontsize=text_fontsize)
# Thick zero contour line
if zero_thick:
cs = plotvars.plot.contour(x, y, field * fmult, [-1e-32, 0],
colors=colors,
linewidths=zero_thick,
linestyles=linestyles, alpha=alpha,
zorder=zorder)
# Titles for dimensions
if titles:
dim_titles(title=title_dims)
# Color bar
if colorbar:
cbar(labels=cbar_labels,
orientation=cb_orient,
position=colorbar_position,
shrink=colorbar_shrink,
title=colorbar_title,
fontsize=colorbar_fontsize,
fontweight=colorbar_fontweight,
text_up_down=colorbar_text_up_down,
text_down_up=colorbar_text_down_up,
drawedges=colorbar_drawedges,
fraction=colorbar_fraction,
thick=colorbar_thick,
levs=clevs,
anchor=colorbar_anchor,
verbose=verbose)
# Title
plotvars.plot.set_title(
title,
y=1.03,
fontsize=title_fontsize,
fontweight=title_fontweight)
# reset plot limits if not a user plot
if plotvars.user_gset == 0:
gset()
############################
# Set axis width if required
############################
if plotvars.axis_width is not None:
for axis in ['top', 'bottom', 'left', 'right']:
plotvars.plot.spines[axis].set_linewidth(plotvars.axis_width)
################################
# Add a master title if reqested
################################
if plotvars.master_title is not None:
location = plotvars.master_title_location
plotvars.master_plot.text(location[0], location[1],
plotvars.master_title,
horizontalalignment='center',
fontweight=plotvars.master_title_fontweight,
fontsize=plotvars.master_title_fontsize)
# Reset map resolution
if plotvars.user_mapset == 0:
mapset()
mapset(resolution=resolution_orig)
##################
# Save or view plot
##################
if plotvars.user_plot == 0:
if verbose:
print('con - saving or viewing plot')
np.seterr(**old_settings) # reset to default numpy error settings
gclose()
[docs]
def mapset(lonmin=None, lonmax=None, latmin=None, latmax=None, proj='cyl',
boundinglat=0, lon_0=0, lat_0=40, resolution='110m', user_mapset=1,
aspect=None):
"""
| mapset sets the mapping parameters.
|
| lonmin=lonmin - minimum longitude
| lonmax=lonmax - maximum longitude
| latmin=latmin - minimum latitude
| latmax=latmax - maximum latitude
| proj=proj - 'cyl' for cylindrical projection. 'npstere' or 'spstere'
| for northern hemisphere or southern hemisphere polar stereographic.
| ortho, merc, moll, robin and lcc are abreviations for orthographic,
| mercator, mollweide, robinson and lambert conformal projections
| 'rotated' for contour plots on the native rotated grid.
|
| boundinglat=boundinglat - edge of the viewable latitudes in a
| stereographic plot
| lon_0=0 - longitude centre of desired map domain in polar
| stereographic and orthogrphic plots
| lat_0=40 - latitude centre of desired map domain in orthogrphic plots
| resolution='110m' - the map resolution - can be one of '110m',
| '50m' or '10m'. '50m' means 1:50,000,000 and not 50 metre.
| user_mapset=user_mapset - variable to indicate whether a user call
| to mapset has been made.
|
| The default map plotting projection is the cyclindrical equidistant
| projection from -180 to 180 in longitude and -90 to 90 in latitude.
| To change the map view in this projection to over the United Kingdom,
| for example, you would use
| mapset(lonmin=-6, lonmax=3, latmin=50, latmax=60)
| or
| mapset(-6, 3, 50, 60)
|
| The limits are -360 to 720 in longitude so to look at the equatorial
| Pacific you could use
| mapset(lonmin=90, lonmax=300, latmin=-30, latmax=30)
| or
| mapset(lonmin=-270, lonmax=-60, latmin=-30, latmax=30)
|
| The default setting for the cylindrical projection is for 1 degree of
| longitude to have the same size as one degree of latitude. When plotting
| a smaller map setting aspect='auto' turns this off and the map fills the
| plot area. Setting aspect to a number a circle will be stretched such that
| the height is num times the width. aspect=1 is the same as aspect='equal'.
|
| The proj parameter accepts 'npstere' and 'spstere' for northern
| hemisphere or southern hemisphere polar stereographic projections.
| In addition to these the boundinglat parameter sets the edge of the
| viewable latitudes and lon_0 sets the centre of desired map domain.
|
|
|
| Map settings are persistent until a new call to mapset is made. To
| reset to the default map settings use mapset().
:Returns:
None
"""
# Set the continent resolution
plotvars.resolution = resolution
if all(val is None for val in [
lonmin, lonmax, latmin, latmax, aspect]) and proj == 'cyl':
plotvars.lonmin = -180
plotvars.lonmax = 180
plotvars.latmin = -90
plotvars.latmax = 90
plotvars.proj = 'cyl'
plotvars.user_mapset = 0
plotvars.aspect = 'equal'
plotvars.plot_xmin = None
plotvars.plot_xmax = None
plotvars.plot_ymin = None
plotvars.plot_ymax = None
return
# Set the aspect ratio
if aspect is None:
aspect = 'equal'
plotvars.aspect = aspect
if lonmin is None:
lonmin = -180
if lonmax is None:
lonmax = 180
if latmin is None:
latmin = -90
if proj == 'merc':
latmin = -80
if latmax is None:
latmax = 90
if proj == 'merc':
latmax = 80
if proj == 'moll':
lonmin = lon_0 - 180
lonmax = lon_0 + 180
plotvars.lonmin = lonmin
plotvars.lonmax = lonmax
plotvars.latmin = latmin
plotvars.latmax = latmax
plotvars.proj = proj
plotvars.boundinglat = boundinglat
plotvars.lon_0 = lon_0
plotvars.lat_0 = lat_0
plotvars.user_mapset = user_mapset
[docs]
def levs(min=None, max=None, step=None, manual=None, extend='both'):
"""
| The levs command manually sets the contour levels.
| min=min - minimum level
| max=max - maximum level
| step=step - step between levels
| manual= manual - set levels manually
| extend='neither', 'both', 'min', or 'max' - colour bar limit extensions
| Use the levs command when a predefined set of levels is required. The
| min, max and step parameters can be used to define a set of levels.
| These can take integer or floating point numbers. If the min and max are specified
| then a step also needs to be specified.
| If just the step is specified then cf-plot will internally try to define a reasonable set
| of levels.
| If colour filled contours are plotted then the default is to extend
| the minimum and maximum contours coloured for out of range values
| - extend='both'.
| Once a user call is made to levs the levels are persistent.
| i.e. the next plot will use the same set of levels.
| Use levs() to reset to undefined levels.
:Returns:
None
"""
if all(val is not None for val in [min, max]) and step is None:
print('\ncfp.levs error: when the min and max are specified a step also needs to be specified\n')
return
if all(val is None for val in [min, max, step, manual]):
plotvars.levels = None
plotvars.levels_min = None
plotvars.levels_max = None
plotvars.levels_step = None
plotvars.levels_extend = 'both'
plotvars.norm = None
plotvars.user_levs = 0
return
if manual is not None:
plotvars.levels = np.array(manual)
plotvars.levels_min = None
plotvars.levels_max = None
plotvars.levels_step = None
# Set the normalization object as we are using potentially unevenly
# spaced levels
ncolors = np.size(plotvars.levels)
if extend == 'both' or extend == 'max':
ncolors = ncolors - 1
plotvars.norm = matplotlib.colors.BoundaryNorm(
boundaries=plotvars.levels, ncolors=ncolors)
plotvars.user_levs = 1
else:
if all(val is not None for val in [min, max, step]):
plotvars.levels_min = min
plotvars.levels_max = max
plotvars.levels_step = step
plotvars.norm = None
if all(isinstance(item, int) for item in [min, max, step]):
lstep = step * 1e-10
levs = (np.arange(min, max + lstep, step, dtype=np.float64))
levs = ((levs * 1e10).astype(np.int64)).astype(np.float64)
levs = (levs / 1e10).astype(np.int64)
plotvars.levels = levs
else:
lstep = step * 1e-10
levs = np.arange(min, max + lstep, step, dtype=np.float64)
levs = (levs * 1e10).astype(np.int64).astype(np.float64)
levs = levs / 1e10
plotvars.levels = levs
plotvars.user_levs = 1
# Check for spurious decimal places due to numeric representation
# and fix if found
for pt in np.arange(np.size(plotvars.levels)):
ndecs = str(plotvars.levels[pt])[::-1].find('.')
if ndecs > 7:
plotvars.levels[pt] = round(plotvars.levels[pt], 7)
# If step only is set then reset user_levs to zero
if step is not None and all(val is None for val in [min, max]):
plotvars.user_levs = 0
plotvars.levels = None
plotvars.levels_step = step
# Check extend has a proper value
if extend not in ['neither', 'min', 'max', 'both']:
errstr = "\n\n extend must be one of 'neither', 'min', 'max', 'both'\n"
raise TypeError(errstr)
plotvars.levels_extend = extend
[docs]
def mapaxis(min=None, max=None, type=None):
"""
| mapaxis is used to work out a sensible set of longitude and latitude
| tick marks and labels. This is an internal routine and is not used
| by the user.
| min=None - minimum axis value
| max=None - maximum axis value
| type=None - 1 = longitude, 2 = latitude
:Returns:
longtitude/latitude ticks and longitude/latitude tick labels
|
|
|
|
|
|
|
"""
degsym = ''
if plotvars.degsym:
degsym = r'$\degree$'
if type == 1:
lonmin = min
lonmax = max
lonrange = lonmax - lonmin
lonstep = 60
if lonrange <= 180:
lonstep = 30
if lonrange <= 90:
lonstep = 10
if lonrange <= 30:
lonstep = 5
if lonrange <= 10:
lonstep = 2
if lonrange <= 5:
lonstep = 1
lons = np.arange(-720, 720 + lonstep, lonstep)
lonticks = []
for lon in lons:
if lon >= lonmin and lon <= lonmax:
lonticks.append(lon)
lonlabels = []
for lon in lonticks:
lon2 = np.mod(lon + 180, 360) - 180
if lon2 < 0 and lon2 > -180:
if lon != 180:
lonlabels.append(str(abs(lon2)) + degsym + 'W')
if lon2 > 0 and lon2 <= 180:
lonlabels.append(str(lon2) + degsym + 'E')
if lon2 == 0:
lonlabels.append('0' + degsym)
if lon == 180 or lon == -180:
lonlabels.append('180' + degsym)
return(lonticks, lonlabels)
if type == 2:
latmin = min
latmax = max
latrange = latmax - latmin
latstep = 30
if latrange <= 90:
latstep = 10
if latrange <= 30:
latstep = 5
if latrange <= 10:
latstep = 2
if latrange <= 5:
latstep = 1
lats = np.arange(-90, 90 + latstep, latstep)
latticks = []
for lat in lats:
if lat >= latmin and lat <= latmax:
latticks.append(lat)
latlabels = []
for lat in latticks:
if lat < 0:
latlabels.append(str(abs(lat)) + degsym + 'S')
if lat > 0:
latlabels.append(str(lat) + degsym + 'N')
if lat == 0:
latlabels.append('0' + degsym)
return(latticks, latlabels)
def timeaxis(dtimes=None):
"""
| timeaxis is used to work out a sensible set of time labels and tick
| marks given a time span This is an internal routine and is not used
| by the user.
| dtimes=None - data times as a CF variable
:Returns:
time ticks and labels
|
|
|
|
|
|
|
"""
time_units = dtimes.Units
time_ticks = []
time_labels = []
axis_label = 'Time'
yearmin = min(dtimes.year.array)
yearmax = max(dtimes.year.array)
tmin = min(dtimes.dtarray)
tmax = max(dtimes.dtarray)
if hasattr(dtimes, 'calendar'):
calendar = dtimes.calendar
else:
calendar = 'standard'
if plotvars.user_gset != 0:
if isinstance(plotvars.xmin, str):
t = cf.Data(cf.dt(plotvars.xmin), units=time_units, calendar=calendar)
yearmin = int(t.year)
t = cf.Data(cf.dt(plotvars.xmax), units=time_units, calendar=calendar)
yearmax = int(t.year)
tmin = cf.dt(plotvars.xmin, calendar=calendar)
tmax = cf.dt(plotvars.xmax, calendar=calendar)
if isinstance(plotvars.ymin, str):
t = cf.Data(cf.dt(plotvars.ymin), units=time_units, calendar=calendar)
yearmin = int(t.year)
t = cf.Data(cf.dt(plotvars.ymax), units=time_units, calendar=calendar)
yearmax = int(t.year)
tmin = cf.dt(plotvars.ymin, calendar=calendar)
tmax = cf.dt(plotvars.ymax, calendar=calendar)
# Years
span = yearmax - yearmin
if span > 4 and span < 3000:
axis_label = 'Time (year)'
tvals = []
if span <= 15:
step = 1
if span > 15:
step = 2
if span > 30:
step = 5
if span > 60:
step = 10
if span > 160:
step = 20
if span > 300:
step = 50
if span > 600:
step = 100
if span > 1300:
step = 200
if plotvars.tspace_year is not None:
step = plotvars.tspace_year
years = np.arange(yearmax / step + 2) * step
tvals = years[np.where((years >= yearmin) & (years <= yearmax))]
# Catch tvals if not properly defined and use gvals to generate some
# year tick marks
if np.size(tvals) < 2:
tvals = gvals(dmin=yearmin, dmax=yearmax)[0]
for year in tvals:
time_ticks.append(np.min(
(cf.Data(cf.dt(str(int(year)) + '-01-01 00:00:00'),
units=time_units, calendar=calendar).array)))
time_labels.append(str(int(year)))
# Months
if yearmax - yearmin <= 4:
months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun',
'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']
# Check number of labels with 1 month steps
tsteps = 0
for year in np.arange(yearmax - yearmin + 1) + yearmin:
for month in np.arange(12):
mytime = cf.dt(str(year) + '-' +
str(month + 1) + '-01 00:00:00', calendar=calendar)
if mytime >= tmin and mytime <= tmax:
tsteps = tsteps + 1
if tsteps < 17:
mvals = np.arange(12)
if tsteps >= 17:
mvals = np.arange(4) * 3
for year in np.arange(yearmax - yearmin + 1) + yearmin:
for month in mvals:
mytime = cf.dt(str(year) + '-' +
str(month + 1) + '-01 00:00:00', calendar=calendar)
if mytime >= tmin and mytime <= tmax:
time_ticks.append(
np.min((cf.Data(mytime, units=time_units, calendar=calendar).array)))
time_labels.append(
str(months[month]) + ' ' + str(int(year)))
# Days and hours
if np.size(time_ticks) <= 2:
myday = cf.dt(int(tmin.year), int(tmin.month), int(tmin.day), calendar=calendar)
not_found = 0
hour_counter = 0
span = 0
while not_found <= 48:
mydate = cf.Data(myday, dtimes.Units) + \
cf.Data(hour_counter, 'hour')
if mydate >= tmin and mydate <= tmax:
span = span + 1
else:
not_found = not_found + 1
hour_counter = hour_counter + 1
step = 1
if span > 13:
step = 1
if span > 13:
step = 4
if span > 25:
step = 6
if span > 100:
step = 12
if span > 200:
step = 24
if span > 400:
step = 48
if span > 800:
step = 96
if plotvars.tspace_hour is not None:
step = plotvars.tspace_hour
if plotvars.tspace_day is not None:
step = plotvars.tspace_day * 24
not_found = 0
hour_counter = 0
axis_label = 'Time (hour)'
if span >= 24:
axis_label = 'Time'
time_ticks = []
time_labels = []
while not_found <= 48:
mytime = cf.Data(myday, dtimes.Units) + cf.Data(hour_counter, 'hour')
if mytime >= tmin and mytime <= tmax:
time_ticks.append(np.min(mytime.array))
label = str(mytime.year) + '-' + str(mytime.month) + '-' + str(mytime.day)
if (hour_counter/24 != int(hour_counter/24)):
label += ' ' + str(mytime.hour) + ':00:00'
time_labels.append(label)
else:
not_found = not_found + 1
hour_counter = hour_counter + step
return(time_ticks, time_labels, axis_label)
[docs]
def ndecs(data=None):
"""
| ndecs finds the number of decimal places in an array. Needed to make the
| colour bar match the contour line labelling.
| data=data - input array of values
:Returns:
| maximum number of necimal places
|
|
|
|
|
|
|
|
"""
maxdecs = 0
for i in range(len(data)):
number = data[i]
a = str(number).split('.')
if np.size(a) == 2:
number_decs = len(a[1])
if number_decs > maxdecs:
maxdecs = number_decs
return maxdecs
[docs]
def axes(xticks=None, xticklabels=None, yticks=None, yticklabels=None,
xstep=None, ystep=None, xlabel=None, ylabel=None, title=None):
"""
| axes is a function to set axes plotting parameters. The xstep and ystep
| parameters are used to label the axes starting at the left hand side and
| bottom of the plot respectively. For tighter control over labelling use
| xticks, yticks to specify the tick positions and xticklabels,
| yticklabels to specify the associated labels.
| xstep=xstep - x axis step
| ystep=ystep - y axis step
| xlabel=xlabel - label for the x-axis
| ylabel=ylabel - label for the y-axis
| xticks=xticks - values for x ticks
| xticklabels=xticklabels - labels for x tick marks
| yticks=yticks - values for y ticks
| yticklabels=yticklabels - labels for y tick marks
| title=None - set title
|
| Use axes() to reset all the axes plotting attributes to the default.
:Returns:
None
"""
if all(val is None for val in [
xticks, yticks, xticklabels, yticklabels, xstep, ystep, xlabel,
ylabel, title]):
plotvars.xticks = None
plotvars.yticks = None
plotvars.xticklabels = None
plotvars.yticklabels = None
plotvars.xstep = None
plotvars.ystep = None
plotvars.xlabel = None
plotvars.ylabel = None
plotvars.title = None
return
plotvars.xticks = xticks
plotvars.yticks = yticks
plotvars.xticklabels = xticklabels
plotvars.yticklabels = yticklabels
plotvars.xstep = xstep
plotvars.ystep = ystep
plotvars.xlabel = xlabel
plotvars.ylabel = ylabel
plotvars.title = title
[docs]
def axes_plot(xticks=None, xticklabels=None, yticks=None, yticklabels=None,
xlabel=None, ylabel=None, title=None):
"""
| axes_plot is a system function to specify axes plotting parameters.
| Use xticks, yticks to specify the tick positions and xticklabels,
| yticklabels to specify the associated labels.
|
| xticks=xticks - values for x ticks
| xticklabels=xticklabels - labels for x tick marks
| yticks=yticks - values for y ticks
| yticklabels=yticklabels - labels for y tick marks
| xlabel=xlabel - label for the x-axis
| ylabel=ylabel - label for the y-axis
| title=None - set title
|
:Returns:
None
"""
if plotvars.title is not None:
title = plotvars.title
title_fontsize = plotvars.title_fontsize
text_fontsize = plotvars.text_fontsize
axis_label_fontsize = plotvars.axis_label_fontsize
if title_fontsize is None:
title_fontsize = 15
if text_fontsize is None:
text_fontsize = 11
if axis_label_fontsize is None:
axis_label_fontsize = 11
axis_label_fontweight = plotvars.axis_label_fontweight
title_fontweight = plotvars.title_fontweight
if (plotvars.plot_type == 1 or plotvars.plot_type == 6) and plotvars.proj == 'cyl':
plot = plotvars.mymap
lon_mid = plotvars.lonmin + (plotvars.lonmax - plotvars.lonmin) / 2.0
plotargs = {'crs': ccrs.PlateCarree()}
else:
plot = plotvars.plot
plotargs = {}
if xlabel is not None:
plotvars.plot.set_xlabel(xlabel, fontsize=axis_label_fontsize,
fontweight=axis_label_fontweight)
if ylabel is not None:
plotvars.plot.set_ylabel(ylabel, fontsize=axis_label_fontsize,
fontweight=axis_label_fontweight)
xticklen = (plotvars.lonmax - plotvars.lonmin)*0.007
yticklen = (plotvars.latmax-plotvars.latmin)*0.014
# set the plot
if (plotvars.plot_type == 1 or plotvars.plot_type == 6):
this_plot = plotvars.mymap
else:
this_plot = plotvars.plot
if plotvars.plot_type == 6 and (plotvars.proj == 'rotated' or plotvars.proj == 'UKCP'):
this_plot = plotvars.plot
# get the plot bounds
l, b, w, h = this_plot.get_position().bounds
lonrange = plotvars.lonmax - plotvars.lonmin
lon_mid = plotvars.lonmin + (plotvars.lonmax - plotvars.lonmin) / 2.0
# Set the ticks and tick labels
if xticks is not None:
# fudge min and max longitude tick positions or the labels wrap
xticks_new = xticks
if lonrange >= 360:
xticks_new[0] = xticks_new[0] + 0.01
xticks_new[-1] = xticks_new[-1] - 0.01
plot.set_xticks(xticks_new, **plotargs)
plot.set_xticklabels(xticklabels,
rotation=plotvars.xtick_label_rotation,
horizontalalignment=plotvars.xtick_label_align)
# Plot a corresponding tick on the top of the plot - cartopy feature?
proj = ccrs.PlateCarree(central_longitude=lon_mid)
if plotvars.plot_type == 1:
for xval in xticks_new:
xpt, ypt = proj.transform_point(xval, plotvars.latmax, ccrs.PlateCarree())
ypt2 = ypt + yticklen
plot.plot([xpt, xpt], [ypt, ypt2], color='k', linewidth=0.8, clip_on=False)
if yticks is not None:
plot.set_yticks(yticks, **plotargs)
plot.set_yticklabels(yticklabels,
rotation=plotvars.ytick_label_rotation,
horizontalalignment=plotvars.ytick_label_align)
# Plot a corresponding tick on the right of the plot - cartopy feature?
if plotvars.plot_type == 1:
proj = ccrs.PlateCarree(central_longitude=lon_mid)
for ytick in yticks:
xpt, ypt = proj.transform_point(plotvars.lonmax-0.001, ytick, ccrs.PlateCarree())
xpt2 = xpt + xticklen
plot.plot([xpt, xpt2], [ypt, ypt], color='k', linewidth=0.8, clip_on=False)
# Set font size and weight
for label in plot.xaxis.get_ticklabels():
label.set_fontsize(axis_label_fontsize)
label.set_fontweight(axis_label_fontweight)
for label in plot.yaxis.get_ticklabels():
label.set_fontsize(axis_label_fontsize)
label.set_fontweight(axis_label_fontweight)
# Title
if title is not None:
plot.set_title(title, y=1.03, fontsize=title_fontsize, fontweight=title_fontweight)
[docs]
def gset(xmin=None, xmax=None, ymin=None, ymax=None,
xlog=False, ylog=False, user_gset=1, twinx=None, twiny=None):
"""
| Set plot limits for all non longitude-latitide plots.
| xmin, xmax, ymin, ymax are all needed to set the plot limits.
| Set xlog/ylog to True or 1 to get a log axis.
|
| xmin=None - x minimum
| xmax=None - x maximum
| ymin=None - y minimum
| ymax=None - y maximum
| xlog=False - log x
| ylog=False - log y
| twinx=None - set to True to make a twin y axis plot
| twiny=None - set to True to make a twin x axis plot
|
| Once a user call is made to gset the plot limits are persistent.
| i.e. the next plot will use the same set of plot limits.
| Use gset() to reset to undefined plot limits i.e. the full range
| of the data.
|
| To set date axes use date strings i.e.
| cfp.gset(xmin = '1970-1-1', xmax = '1999-12-31', ymin = 285,
| ymax = 295)
|
| Note the correct date format is 'YYYY-MM-DD' or 'YYYY-MM-DD HH:MM:SS'
| anything else will give unexpected results.
:Returns:
None
|
|
|
|
"""
plotvars.user_gset = user_gset
if all(val is None for val in [xmin, xmax, ymin, ymax]):
plotvars.xmin = None
plotvars.xmax = None
plotvars.ymin = None
plotvars.ymax = None
plotvars.xlog = False
plotvars.ylog = False
plotvars.twinx = False
plotvars.twiny = False
plotvars.user_gset = 0
return
bcount = 0
for val in [xmin, xmax, ymin, ymax]:
if val is None:
bcount = bcount + 1
if bcount != 0 and bcount != 4:
errstr = 'gset error\n'
errstr += 'xmin, xmax, ymin, ymax all need to be passed to gset\n'
errstr += 'to set the plot limits\n'
raise Warning(errstr)
plotvars.xmin = xmin
plotvars.xmax = xmax
plotvars.ymin = ymin
plotvars.ymax = ymax
plotvars.xlog = xlog
plotvars.ylog = ylog
# Check if any axes are time strings
time_xstr = False
time_ystr = False
try:
float(xmin)
except Exception:
time_xstr = True
try:
float(ymin)
except Exception:
time_ystr = True
# Set plot limits
if plotvars.plot is not None and twinx is None and twiny is None:
if not time_xstr and not time_ystr:
plotvars.plot.axis(
[plotvars.xmin, plotvars.xmax, plotvars.ymin, plotvars.ymax])
if plotvars.xlog:
plotvars.plot.set_xscale('log')
if plotvars.ylog:
plotvars.plot.set_yscale('log')
# Set twinx or twiny if requested
if twinx is not None:
plotvars.twinx = twinx
if twiny is not None:
plotvars.twiny = twiny
[docs]
def gopen(rows=1, columns=1, user_plot=1, file='cfplot.png',
orientation='landscape', figsize=[11.7, 8.3],
left=None, right=None, top=None, bottom=None, wspace=None,
hspace=None, dpi=None, user_position=False):
"""
| gopen is used to open a graphic file.
|
| rows=1 - number of plot rows on the page
| columns=1 - number of plot columns on the page
| user_plot=1 - internal plot variable - do not use.
| file='cfplot.png' - default file name
| orientation='landscape' - orientation - also takes 'portrait'
| figsize=[11.7, 8.3] - figure size in inches
| left=None - left margin in normalised coordinates - default=0.12
| right=None - right margin in normalised coordinates - default=0.92
| top=None - top margin in normalised coordinates - default=0.08
| bottom=None - bottom margin in normalised coordinates - default=0.08
| wspace=None - width reserved for blank space between subplots - default=0.2
| hspace=None - height reserved for white space between subplots - default=0.2
| dpi=None - resolution in dots per inch
| user_position=False - set to True to supply plot position via gpos
| xmin, xmax, ymin, ymax values
:Returns:
None
|
|
|
|
|
"""
# Set values in globals
plotvars.rows = rows
plotvars.columns = columns
if file != 'cfplot.png':
plotvars.file = file
plotvars.orientation = orientation
plotvars.user_plot = user_plot
plotvars.gpos_called = False
# Set user defined plot area to None
plotvars.plot_xmin = None
plotvars.plot_xmax = None
plotvars.plot_ymin = None
plotvars.plot_ymax = None
if left is None:
left = 0.12
if right is None:
right = 0.92
if top is None:
top = 0.95
if bottom is None:
bottom = 0.08
if rows >= 3:
bottom = 0.1
if wspace is None:
wspace = 0.2
if hspace is None:
hspace = 0.2
if rows >= 3:
hspace = 0.5
if orientation != 'landscape':
if orientation != 'portrait':
errstr = 'gopen error\n'
errstr += 'orientation incorrectly set\n'
errstr += 'input value was ' + orientation + '\n'
errstr += 'Valid options are portrait or landscape\n'
raise Warning(errstr)
# Set master plot size
if orientation == 'landscape':
plotvars.master_plot = plot.figure(figsize=(figsize[0], figsize[1]))
else:
plotvars.master_plot = plot.figure(figsize=(figsize[1], figsize[0]))
# Set margins
plotvars.master_plot.subplots_adjust(
left=left,
right=right,
top=top,
bottom=bottom,
wspace=wspace,
hspace=hspace)
# Set initial subplot
if user_position is False and rows == 1 and columns == 1:
gpos(pos=1)
# Change tick length for plots > 2x2
if (columns > 2 or rows > 2):
matplotlib.rcParams['xtick.major.size'] = 2
matplotlib.rcParams['ytick.major.size'] = 2
# Set image resolution
if dpi is not None:
plotvars.dpi = dpi
[docs]
def gclose(view=True):
"""
| gclose saves a graphics file. The default is to view the file as well
| - use view = False to turn this off.
| view = True - view graphics file
:Returns:
None
|
|
|
|
|
|
|
|
|
"""
# Reset the user_plot variable to off
plotvars.user_plot = 0
# Test for python or ipython
interactive = False
try:
__IPYTHON__
interactive = True
except NameError:
interactive = False
if matplotlib.is_interactive():
interactive = True
# Remove whitespace if requested
saveargs = {}
if plotvars.tight:
saveargs = {'bbox_inches': 'tight'}
file = plotvars.file
if file is not None:
# Save a file
type = 1
if file[-3:] == '.ps':
type = 1
if file[-4:] == '.eps':
type = 1
if file[-4:] == '.png':
type = 1
if file[-4:] == '.pdf':
type = 1
if type is None:
file = file + '.png'
plotvars.master_plot.savefig(
file, orientation=plotvars.orientation, dpi=plotvars.dpi, **saveargs)
plot.close()
else:
if plotvars.viewer == 'display' and interactive is False:
# Use Imagemagick display command if this exists
disp = which('display')
if disp is not None:
tfile = 'cfplot.png'
plotvars.master_plot.savefig(
tfile, orientation=plotvars.orientation, dpi=plotvars.dpi, **saveargs)
matplotlib.pyplot.ioff()
subprocess.Popen([disp, tfile])
else:
plotvars.viewer = 'matplotlib'
if plotvars.viewer == 'matplotlib' or interactive:
# Use Matplotlib viewer
matplotlib.pyplot.ion()
plot.show()
# Reset plotting
plotvars.plot = None
plotvars.twinx = None
plotvars.twiny = None
plotvars.plot_xmin = None
plotvars.plot_xmax = None
plotvars.plot_ymin = None
plotvars.plot_ymax = None
plotvars.graph_xmin = None
plotvars.graph_xmax = None
plotvars.graph_ymin = None
plotvars.graph_ymax = None
plotvars.gpos_called = False
plotvars.mymap = None
plotvars.titles_con_called = False
[docs]
def gpos(pos=1, xmin=None, xmax=None, ymin=None, ymax=None):
"""
| Set plot position. Plots start at top left and increase by one each plot
| to the right. When the end of the row has been reached then the next
| plot will be the leftmost plot on the next row down.
| pos=pos - plot position
|
| The following four parameters are used to get full user control
| over the plot position. In addition to these cfp.gopen
| must have the user_position=True parameter set.
| xmin=None xmin in normalised coordinates
| xmax=None xmax in normalised coordinates
| ymin=None ymin in normalised coordinates
| ymax=None ymax in normalised coordinates
|
|
:Returns:
None
|
|
|
|
|
|
|
|
"""
# Reset mymap
plotvars.mymap = None
# Check inputs are okay
if pos < 1 or pos > plotvars.rows * plotvars.columns:
errstr = 'pos error - pos out of range:\n range = 1 - '
errstr = errstr + str(plotvars.rows * plotvars.columns)
errstr = errstr + '\n input pos was ' + str(pos)
errstr = errstr + '\n'
raise Warning(errstr)
user_pos = False
if all(val is not None for val in [xmin, xmax, ymin, ymax]):
user_pos = True
plotvars.plot_xmin = xmin
plotvars.plot_xmax = xmax
plotvars.plot_ymin = ymin
plotvars.plot_ymax = ymax
# Reset any accumulated muliple graph limits
plotvars.graph_xmin = None
plotvars.graph_xmax = None
plotvars.graph_ymin = None
plotvars.graph_ymax = None
# Set gpos_called
plotvars.gpos_called = True
# Reset titles_con_called
plotvars.titles_con_called = False
if user_pos is False:
plotvars.plot = plotvars.master_plot.add_subplot(
plotvars.rows, plotvars.columns, pos)
else:
delta_x = plotvars.plot_xmax - plotvars.plot_xmin
delta_y = plotvars.plot_ymax - plotvars.plot_ymin
plotvars.plot = plotvars.master_plot.add_axes([plotvars.plot_xmin,
plotvars.plot_ymin,
delta_x, delta_y])
plotvars.plot.tick_params(which='both', direction='out', right=True, top=True)
# Set position in global variables
plotvars.pos = pos
# Reset contour levels if they are not defined by the user
if plotvars.user_levs == 0:
if plotvars.levels_step is None:
levs()
else:
levs(step=plotvars.levels_step)
[docs]
def pcon(mb=None, km=None, h=7.0, p0=1000):
"""
| pcon is a function for converting pressure to height in kilometers and
| vice-versa. This function uses the equation P=P0exp(-z/H) to translate
| between pressure and height. In pcon the surface pressure P0 is set to
| 1000.0mb and the scale height H is set to 7.0. The value of H can vary
| from 6.0 in the polar regions to 8.5 in the tropics as well as
| seasonally. The value of P0 could also be said to be 1013.25mb rather
| than 1000.0mb.
| As this relationship is approximate:
| (i) Only use this for making the axis labels on y axis pressure plots
| (ii) Put the converted axis on the right hand side to indicate that
| this isn't the primary unit of measure
| print cfp.pcon(mb=[1000, 300, 100, 30, 10, 3, 1, 0.3])
| [0. 8.42780963 16.11809565 24.54590528 32.2361913
| 40.66400093 48.35428695, 56.78209658]
| mb=None - input pressure
| km=None - input height
| h=7.0 - default value for h
| p0=1000 - default value for p0
:Returns:
| pressure(mb) if height(km) input,
| height(km) if pressure(mb) input
"""
if all(val is None for val in [mb, km]) == 2:
errstr = 'pcon error - pcon must have mb or km input\n'
raise Warning(errstr)
if mb is not None:
return h * (np.log(p0) - np.log(mb))
if km is not None:
return np.exp(-1.0 * (np.array(km) / h - np.log(p0)))
[docs]
def supscr(text=None):
"""
| supscr - add superscript text formatting for ** and ^
| This is an internal routine used in titles and colour bars
| and not used by the user.
| text=None - input text
:Returns:
Formatted text
|
|
|
|
|
|
|
"""
if text is None:
errstr = '\n supscr error - supscr must have text input\n'
raise Warning(errstr)
tform = ''
sup = 0
for i in text:
if (i == '^'):
sup = 2
if (i == '*'):
sup = sup + 1
if (sup == 0):
tform = tform + i
if (sup == 1):
if (i not in '*'):
tform = tform + '*' + i
sup = 0
if (sup == 3):
if i in '-0123456789':
tform = tform + i
else:
tform = tform + '}$' + i
sup = 0
if (sup == 2):
tform = tform + '$^{'
sup = 3
if (sup == 3):
tform = tform + '}$'
tform = tform.replace('m2', 'm$^{2}$')
tform = tform.replace('m3', 'm$^{3}$')
tform = tform.replace('m-2', 'm$^{-2}$')
tform = tform.replace('m-3', 'm$^{-3}$')
tform = tform.replace('s-1', 's$^{-1}$')
tform = tform.replace('s-2', 's$^{-2}$')
return tform
[docs]
def gvals(dmin=None, dmax=None, mystep=None, mod=True):
"""
| gvals - work out a sensible set of values between two limits
| This is an internal routine used for contour levels and axis
| labelling and is not generally used by the user.
| dmin = None - minimum
| dmax = None - maximum
| mystep = None - use this step
| mod = True - modify data to make use of a multipler
|
|
|
|
|
|
"""
# Copies of inputs as these might be changed
dmin1 = deepcopy(dmin)
dmax1 = deepcopy(dmax)
# Swap values if dmin1 > dmax1
if dmax1 < dmin1:
dmin1, dmax1 = dmax1, dmin1
# Data range
data_range = dmax1 - dmin1
# field multiplier
mult = 0
vals = None
# Return some values if dmin1 = dmax1
if dmin1 == dmax1:
vals = np.array([dmin1 - 1, dmin1, dmin1 + 1])
mult = 0
return vals, mult
# Modify if requested or if out of range 0.001 to 2000000
if data_range < 0.001:
while dmax1 <= 3:
dmin1 = dmin1 * 10.0
dmax1 = dmax1 * 10.0
data_range = dmax1 - dmin1
mult = mult - 1
if data_range > 2000000:
while dmax1 > 10:
dmin1 = dmin1 / 10.0
dmax1 = dmax1 / 10.0
data_range = dmax1 - dmin1
mult = mult + 1
if data_range >= 0.001 and data_range <= 2000000:
# Calculate an appropriate step
step = None
test_steps = [0.0001, 0.0002, 0.0005, 0.001, 0.002, 0.005, 0.01, 0.02, 0.05, 0.1,
0.2, 0.5, 1, 2, 5, 10, 20, 50, 100, 200, 500, 1000, 2000, 5000, 10000,
20000, 50000, 100000]
if mystep is not None:
step = mystep
else:
for val in test_steps:
nvals = data_range / val
if val < 1:
if nvals > 8:
step = val
else:
if nvals > 11:
step = val
# Return an error if no step found
if step is None:
errstr = '\n\n cfp.gvals - no valid step values found \n\n'
errstr += 'cfp.gvals(' + str(dmin1) + ',' + str(dmax1) + ')\n\n'
raise Warning(errstr)
# values < 0.0
vals = None
vals1 = None
if dmin1 < 0.0:
vals1 = (np.arange(-dmin1 / step) * -step)[::-1] - step
# values >= 0.0
vals2 = None
if dmax1 >= 0.0:
vals2 = np.arange(dmax1 / step + 1) * step
if vals1 is not None and vals2 is None:
vals = vals1
if vals2 is not None and vals1 is None:
vals = vals2
if vals1 is not None and vals2 is not None:
vals = np.concatenate((vals1, vals2))
# Round off decimal numbers so that
# (np.arange(4) * -0.1)[3] = -0.30000000000000004 gives -0.3 as expected
if step < 1:
vals = vals.round(6)
# Change values to integers for values >= 1
if step >= 1:
vals = vals.astype(int)
pts = np.where(np.logical_and(vals >= dmin1, vals <= dmax1))
if np.min(pts) > -1:
vals = vals[pts]
if mod is False:
vals = vals * 10**mult
mult = 0
# Catch if no values have been defined
if vals is None:
vals = np.array([dmin, dmax])
return(vals, mult)
[docs]
def cf_data_assign(f=None, colorbar_title=None, verbose=None, rotated_vect=False):
"""
| Check cf input data is okay and return data for contour plot.
| This is an internal routine not used by the user.
| f=None - input cf field
| colorbar_title=None - input colour bar title
| rotated vect=False - return 1D x and y for rotated plot vectors
| verbose=None - set to 1 to get a verbose idea of what the
| cf_data_assign is doing
:Returns:
| f - data for contouring
| x - x coordinates of data (optional)
| y - y coordinates of data (optional)
| ptype - plot type
| colorbar_title - colour bar title
| xlabel - x label for plot
| ylabel - y label for plot
|
|
|
|
|
"""
# Check input data has the correct number of dimensions
# Take into account rotated pole fields having extra dimensions
ndim = len(f.domain_axes().filter_by_size(cf.gt(1)))
if f.ref('grid_mapping_name:rotated_latitude_longitude', default=False) is False:
if (ndim > 2 or ndim < 1):
print('')
if (ndim > 2):
errstr = 'cf_data_assign error - data has too many dimensions'
if (ndim < 1):
errstr = 'cf_data_assign error - data has too few dimensions'
errstr += '\n cf-plot requires one or two dimensional data\n'
for mydim in list(f.dimension_coordinates()):
sn = getattr(f.construct(mydim), 'standard_name', False)
ln = getattr(f.construct(mydim), 'long_name', False)
if sn:
errstr = errstr + \
str(mydim) + ',' + str(sn) + ',' + \
str(f.construct(mydim).size) + '\n'
else:
if ln:
errstr = errstr + \
str(mydim) + ',' + str(ln) + ',' + \
str(f.construct(mydim).size) + '\n'
raise Warning(errstr)
# Set up data arrays and variables
lons = None
lats = None
height = None
time = None
xlabel = ''
ylabel = ''
has_lons = False
has_lats = False
has_height = False
has_time = False
xpole = None
ypole = None
ptype = None
field = None
x = None
y = None
# Check for multiple Z coordinates
myz = find_z(f)
# Extract coordinate data if a matching CF standard_name or axis is found
for mycoord in f.coords():
c = f.coord(mycoord)
if c.X:
if verbose:
print('lons -', mydim)
lons = np.squeeze(f.construct(mycoord).array)
if np.size(lons) > 1:
has_lons = True
if c.Y:
if verbose:
print('lats -', mydim)
lats = np.squeeze(f.construct(mycoord).array)
if np.size(lats) > 1:
has_lats = True
if c.Z:
if verbose:
print('height -', mydim)
height = np.squeeze(f.construct(mycoord).array)
if np.size(height) > 1:
has_height = True
if c.T:
if verbose:
print('time -', mydim)
time = np.squeeze(f.construct(mycoord).array)
if np.size(time) > 1:
has_time = True
# assign field data
field = np.squeeze(f.array)
# Change Boolean data to integer
if str(f.dtype) == 'bool':
warnstr = '\n\n\n Warning - boolean data found - converting to integers\n\n\n'
print(warnstr)
g = deepcopy(f)
g.dtype = int
field = np.squeeze(g.array)
# Check what plot type is required.
# 0=simple contour plot, 1=map plot, 2=latitude-height plot,
# 3=longitude-time plot, 4=latitude-time plot.
if has_lons and has_lats:
ptype = 1
x = lons
y = lats
if has_lats and has_height:
ptype = 2
x = lats
y = height
xname = cf_var_name(field=f, dim='Y')
xunits = str(getattr(f.construct('Y'), 'Units', ''))
if xunits == 'degrees_north':
xunits = 'degrees'
if xunits != '':
xlabel = xname + ' (' + xunits + ')'
else:
xlabel = xname
yname = cf_var_name(field=f, dim=myz)
yunits = str(getattr(f.construct(myz), 'Units', ''))
if yunits != '':
ylabel = yname + ' (' + yunits + ')'
else:
ylabel = yname
if has_lons and has_height:
ptype = 3
x = lons
y = height
xname = cf_var_name(field=f, dim='X')
xunits = str(getattr(f.construct('X'), 'Units', ''))
if xunits == 'degrees_east':
xunits = 'degrees'
if xunits != '':
xlabel = xname + ' (' + xunits + ')'
else:
xlabel = xname
yname = cf_var_name(field=f, dim=myz)
yunits = str(getattr(f.construct(myz), 'Units', ''))
if yunits != '':
ylabel = yname + ' (' + yunits + ')'
else:
ylabel = yname
if has_lons and has_time:
ptype = 4
x = lons
y = time
xname = cf_var_name(field=f, dim='X')
xunits = str(getattr(f.construct('X'), 'Units', ''))
if xunits == 'degrees_east':
xunits = 'degrees'
if xunits != '':
xlabel = xname + ' (' + xunits + ')'
else:
xlabel = xname
yname = cf_var_name(field=f, dim='T')
yunits = str(getattr(f.construct('T'), 'Units', ''))
if yunits != '':
ylabel = yname + ' (' + yunits + ')'
else:
ylabel = yname
if has_lats and has_time:
ptype = 5
x = lats
y = time
xname = cf_var_name(field=f, dim='Y')
xunits = str(getattr(f.construct('Y'), 'Units', ''))
if xunits == 'degrees_north':
xunits = 'degrees'
if xunits != '':
xlabel = xname + ' (' + xunits + ')'
else:
xlabel = xname
yname = cf_var_name(field=f, dim='T')
yunits = str(getattr(f.construct('T'), 'Units', ''))
if yunits != '':
ylabel = yname + ' (' + yunits + ')'
else:
ylabel = yname
# time height plot
if has_height and has_time:
ptype = 7
x = time
y = height
xname = cf_var_name(field=f, dim='T')
xunits = str(getattr(f.construct('T'), 'Units', ''))
if xunits != '':
xlabel = xname + ' (' + xunits + ')'
else:
xlabel = xname
yname = cf_var_name(field=f, dim='Z')
yunits = str(getattr(f.construct('Z'), 'Units', ''))
if yunits != '':
ylabel = yname + ' (' + yunits + ')'
else:
ylabel = yname
# Rotate array to get it as time vs height
field = np.rot90(field)
field = np.flipud(field)
# Rotated pole
if f.ref('grid_mapping_name:rotated_latitude_longitude', default=False):
ptype = 6
rotated_pole = f.ref('grid_mapping_name:rotated_latitude_longitude')
xpole = rotated_pole['grid_north_pole_longitude']
ypole = rotated_pole['grid_north_pole_latitude']
# Extract grid x and y coordinates
for mydim in list(f.dimension_coordinates()):
name = cf_var_name(field=f, dim=mydim)
if name in ['grid_longitude', 'longitude', 'x']:
x = np.squeeze(f.construct(mydim).array)
xunits = str(getattr(f.construct(mydim), 'units', ''))
xlabel = cf_var_name(field=f, dim=mydim)
if name in ['grid_latitude', 'latitude', 'y']:
y = np.squeeze(f.construct(mydim).array)
# Flip y and data if reversed
if y[0] > y[-1]:
y = y[::-1]
field = np.flipud(field)
yunits = str(getattr(f.construct(mydim), 'Units', ''))
ylabel = cf_var_name(field=f, dim=mydim) + yunits
# Extract auxiliary lons and lats if they exist
if ptype == 1 or ptype is None:
if plotvars.proj != 'rotated' and not rotated_vect:
aux_lons = False
aux_lats = False
for mydim in list(f.auxiliary_coordinates()):
name = cf_var_name(field=f, dim=mydim)
if name in ['longitude']:
xpts = np.squeeze(f.construct(mydim).array)
aux_lons = True
if name in ['latitude']:
ypts = np.squeeze(f.construct(mydim).array)
aux_lats = True
if aux_lons and aux_lats:
x = xpts
y = ypts
ptype = 1
# UKCP grid
if f.ref('grid_mapping_name:transverse_mercator', default=False):
ptype = 1
field = np.squeeze(f.array)
# Find the auxiliary lons and lats if provided
has_lons = False
has_lats = False
for mydim in list(f.auxiliary_coordinates()):
name = cf_var_name(field=f, dim=mydim)
if name in ['longitude']:
x = np.squeeze(f.construct(mydim).array)
has_lons = True
if name in ['latitude']:
y = np.squeeze(f.construct(mydim).array)
has_lats = True
# Calculate lons and lats if no auxiliary data for these
if not has_lons or not has_lats:
xpts = f.construct('X').array
ypts = f.construct('Y').array
field = np.squeeze(f.array)
ref = f.ref('grid_mapping_name:transverse_mercator')
false_easting = ref['false_easting']
false_northing = ref['false_northing']
central_longitude = ref['longitude_of_central_meridian']
central_latitude = ref['latitude_of_projection_origin']
scale_factor = ref['scale_factor_at_central_meridian']
# Set the transform
transform = ccrs.TransverseMercator(false_easting=false_easting,
false_northing=false_northing,
central_longitude=central_longitude,
central_latitude=central_latitude,
scale_factor=scale_factor)
# Calculate the longitude and latitude points
xvals, yvals = np.meshgrid(xpts, ypts)
points = ccrs.PlateCarree().transform_points(transform, xvals, yvals)
x = np.array(points)[:, :, 0]
y = np.array(points)[:, :, 1]
# None of the above
if ptype is None:
ptype = 0
data_axes = f.get_data_axes()
count = 1
for d in data_axes:
try:
c = f.coordinate(filter_by_axis = [d])
if np.size(c.array) > 1:
if count == 1:
y = c
mycoord = 'dimensioncoordinate'+str(d[-1])
yunits = str(getattr(f.coord(mycoord), 'Units', ''))
if yunits != '':
yunits = '(' + yunits + ')'
ylabel = cf_var_name(field=f, dim=mycoord) + yunits
elif count == 2:
x = c
mycoord = 'dimensioncoordinate'+str(d[-1])
xunits = str(getattr(f.coord(mycoord), 'units', ''))
if xunits != '':
xunits = '(' + xunits + ')'
xlabel = cf_var_name(field=f, dim=mycoord) + xunits
count += 1
except ValueError:
errstr = "\n\ncf_data_assign - cannot find data to return\n\n"
errstr += str(f.constructs.domain_axis_identity(d)) + "\n\n"
raise Warning(errstr)
# Assign colorbar_title
if (colorbar_title is None):
colorbar_title = 'No Name'
if hasattr(f, 'id'):
colorbar_title = f.id
nc = f.nc_get_variable(None)
if nc:
colorbar_title = f.nc_get_variable()
if hasattr(f, 'short_name'):
colorbar_title = f.short_name
if hasattr(f, 'long_name'):
colorbar_title = f.long_name
if hasattr(f, 'standard_name'):
colorbar_title = f.standard_name
if hasattr(f, 'Units'):
if str(f.Units) == '':
colorbar_title = colorbar_title + ''
else:
colorbar_title = colorbar_title + \
'(' + supscr(str(f.Units)) + ')'
# Return data
return(field, x, y, ptype, colorbar_title, xlabel, ylabel, xpole, ypole)
[docs]
def check_data(field=None, x=None, y=None):
"""
| check_data - check user input contour data is correct.
| This is an internal routine and is not used by the user.
|
| field=None - field
| x=None - x points for field
| y=None - y points for field
|
|
|
|
|
|
"""
# Input error trapping
args = True
errstr = '\n'
if np.size(field) == 1:
if field is None:
errstr = errstr + 'con error - a field for contouring must be '
errstr += 'passed with the f= flag\n'
args = False
if np.size(x) == 1:
if x is None:
x = np.arange(np.shape(field)[1])
if np.size(y) == 1:
if y is None:
y = np.arange(np.shape(field)[0])
if not args:
raise Warning(errstr)
# Check input dimensions look okay.
# All inputs 2D
if np.ndim(field) == 2 and np.ndim(x) == 2 and np.ndim(y) == 2:
xpts = np.shape(field)[1]
ypts = np.shape(field)[0]
if xpts != np.shape(x)[1] or xpts != np.shape(y)[1]:
args = False
if ypts != np.shape(x)[0] or ypts != np.shape(y)[0]:
args = False
if args:
return
# Field x and y all 1D
if np.ndim(field) == 1 and np.ndim(x) == 1 and np.ndim(y) == 1:
if np.size(x) != np.size(field):
args = False
if np.size(y) != np.size(field):
args = False
if args:
return
# Field 2D, x and y 1D
if np.ndim(field) != 2:
args = False
if np.ndim(x) != 1:
args = False
if np.ndim(y) != 1:
args = False
if np.ndim(field) == 2:
if np.size(x) != np.shape(field)[1]:
args = False
if np.size(y) != np.shape(field)[0]:
args = False
if args is False:
errstr = errstr + 'Input arguments incorrectly shaped:\n'
errstr = errstr + 'x has shape:' + str(np.shape(x)) + '\n'
errstr = errstr + 'y has shape:' + str(np.shape(y)) + '\n'
errstr = errstr + 'field has shape' + str(np.shape(field)) + '\n\n'
errstr = errstr + 'Expected x=xpts, y=ypts, field=(ypts,xpts)\n'
errstr = errstr + 'x=npts, y=npts, field=npts\n'
errstr = errstr + \
'or x=[ypts, xpts], y=[ypts, xpts], field=[ypts, xpts]\n'
raise Warning(errstr)
[docs]
def cscale(scale=None, ncols=None, white=None, below=None,
above=None, reverse=False, uniform=False):
"""
| cscale - choose and manipulate colour maps. Around 200 colour scales are
| available - see the gallery section for more details.
|
| scale=None - name of colour map
| ncols=None - number of colours for colour map
| white=None - change these colours to be white
| below=None - change the number of colours below the mid point of
| the colour scale to be this
| above=None - change the number of colours above the mid point of
| the colour scale to be this
| reverse=False - reverse the colour scale
| uniform=False - produce a uniform colour scale.
| For example: if below=3 and above=10 are specified
| then initially below=10 and above=10 are used. The
| colour scale is then cropped to use scale colours
| 6 to 19. This produces a more uniform intensity colour
| scale than one where all the blues are compressed into
| 3 colours.
|
|
| Personal colour maps are available by saving the map as red green blue
| to a file with a set of values on each line.
|
|
| Use cscale() To reset to the default settings.
|
:Returns:
None
|
|
|
|
"""
# If no map requested reset to default
if scale is None:
scale = 'scale1'
plotvars.cscale_flag = 0
return
else:
plotvars.cs_user = scale
plotvars.cscale_flag = 1
vals = [ncols, white, below, above]
if any(val is not None for val in vals):
plotvars.cscale_flag = 2
if reverse is not False or uniform is not False:
plotvars.cscale_flag = 2
if scale == 'scale1' or scale == '':
if scale == 'scale1':
myscale = cscale1
if scale == 'viridis':
myscale = viridis
# convert cscale1 or viridis from hex to rgb
r = []
g = []
b = []
for myhex in myscale:
myhex = myhex.lstrip('#')
mylen = len(myhex)
rgb = tuple(int(myhex[i:i + mylen // 3], 16)
for i in range(0, mylen, mylen // 3))
r.append(rgb[0])
g.append(rgb[1])
b.append(rgb[2])
else:
package_path = os.path.dirname(__file__)
file = os.path.join(package_path, 'colourmaps/' + scale + '.rgb')
if os.path.isfile(file) is False:
if os.path.isfile(scale) is False:
errstr = '\ncscale error - colour scale not found:\n'
errstr = errstr + 'File ' + file + ' not found\n'
errstr = errstr + 'File ' + scale + ' not found\n'
raise Warning(errstr)
else:
file = scale
# Read in rgb values and convert to hex
f = open(file, 'r')
lines = f.read()
lines = lines.splitlines()
r = []
g = []
b = []
for line in lines:
vals = line.split()
r.append(int(vals[0]))
g.append(int(vals[1]))
b.append(int(vals[2]))
# Reverse the colour scale if requested
if reverse:
r = r[::-1]
g = g[::-1]
b = b[::-1]
# Interpolate to a new number of colours if requested
if ncols is not None:
x = np.arange(np.size(r))
xnew = np.linspace(0, np.size(r) - 1, num=ncols, endpoint=True)
f_red = interpolate.interp1d(x, r)
f_green = interpolate.interp1d(x, g)
f_blue = interpolate.interp1d(x, b)
r = f_red(xnew)
g = f_green(xnew)
b = f_blue(xnew)
# Change the number of colours below and above the mid-point if requested
if below is not None or above is not None:
# Mid-point of colour scale
npoints = np.size(r) // 2
# Below mid point x locations
x_below = []
lower = 0
if below == 1:
x_below = 0
if below is not None:
lower = below
if below is None:
lower = npoints
if below is not None and uniform:
lower = max(above, below)
if (lower > 1):
x_below = ((npoints - 1) / float(lower - 1)) * np.arange(lower)
# Above mid point x locations
x_above = []
upper = 0
if above == 1:
x_above = npoints * 2 - 1
if above is not None:
upper = above
if above is None:
upper = npoints
if above is not None and uniform:
upper = max(above, below)
if (upper > 1):
x_above = ((npoints - 1) / float(upper - 1)) * \
np.arange(upper) + npoints
# Append new colour positions
xnew = np.append(x_below, x_above)
# Interpolate to new colour scale
xpts = np.arange(np.size(r))
f_red = interpolate.interp1d(xpts, r)
f_green = interpolate.interp1d(xpts, g)
f_blue = interpolate.interp1d(xpts, b)
r = f_red(xnew)
g = f_green(xnew)
b = f_blue(xnew)
# Reset colours if uniform is set
if uniform:
mid_pt = max(below, above)
r = r[mid_pt - below:mid_pt + above]
g = g[mid_pt - below:mid_pt + above]
b = b[mid_pt - below:mid_pt + above]
# Convert to hex
hexarr = []
for col in np.arange(np.size(r)):
hexarr.append('#%02x%02x%02x' % (int(r[col]), int(g[col]), int(b[col])))
# White requested colour positions
if white is not None:
if np.size(white) == 1:
hexarr[white] = '#ffffff'
else:
for col in white:
hexarr[col] = '#ffffff'
# Set colour scale
plotvars.cs = hexarr
[docs]
def cscale_get_map():
"""
| cscale_get_map - return colour map for use in contour plots.
| This depends on the colour bar extensions
| This is an internal routine and is not used by the user.
|
|
:Returns:
colour map
|
|
|
|
|
"""
cscale_ncols = np.size(plotvars.cs)
if (plotvars.levels_extend == 'both'):
colmap = plotvars.cs[1:cscale_ncols - 1]
if (plotvars.levels_extend == 'min'):
colmap = plotvars.cs[1:]
if (plotvars.levels_extend == 'max'):
colmap = plotvars.cs[:cscale_ncols - 1]
if (plotvars.levels_extend == 'neither'):
colmap = plotvars.cs
return (colmap)
[docs]
def bfill(f=None, x=None, y=None, clevs=False, lonlat=None, bound=False,
alpha=1.0, single_fill_color=None, white=True, zorder=4, fast=None, transform=False,
orca=False):
"""
| bfill - block fill a field with colour rectangles
| This is an internal routine and is not generally used by the user.
|
| f=None - field
| x=None - x points for field
| y=None - y points for field
| clevs=None - levels for filling
| lonlat=None - longitude and latitude data
| bound=False - x and y are cf data boundaries
| alpha=alpha - transparency setting 0 to 1
| white=True - colour unplotted areas white
| single_fill_color=None - colour for a blockfill between two levels
| - makes maplotlib named colours or
| - hexadecimal notation - '#d3d3d3' for grey
| zorder=4 - plotting order
| fast=None - use fast plotting with pcolormesh which is useful for larger datasets
| transform=False - map transform supplied by calling routine
| orca=False - data is orca data
|
:Returns:
None
|
|
|
|
"""
# Set lonlat if not specified
lonlat = False
if plotvars.plot_type == 1:
lonlat = True
# If single_fill_color is defined then turn off whiting out the background.
if single_fill_color is not None:
white = False
# Set 2D lon lat if data is that format
two_d = False
if not isinstance(f, cf.Field):
if np.ndim(x) == 2 and np.ndim(x) == 2:
two_d = True
# Set the default map coordinates for the data to be PlateCarree
plotargs = {}
if lonlat:
plotargs = {'transform': ccrs.PlateCarree()}
# Set the field
if isinstance(f, cf.Field):
field = f.array
else:
field = f
levels = np.array(deepcopy(clevs)).astype('float')
# Generate a Matplotlib colour map
#if single_fill_color is None:
# cols = plotvars.cs
#else:
# cols = single_fill_color
#cmap = matplotlib.colors.ListedColormap(cols)
#levels_orig = deepcopy(levels)
#if single_fill_color is None:
# if plotvars.levels_extend == 'both' or plotvars.levels_extend == 'min':
# levels = np.insert(levels, 0, -1e30)
# if plotvars.levels_extend == 'both' or plotvars.levels_extend == 'max':
# levels = np.append(levels, 1e30)
# if plotvars.levels_extend == 'both' or plotvars.levels_extend == 'min':
# cmap.set_under(plotvars.cs[0])
# cols = cols[1:]
# if plotvars.levels_extend == 'both' or plotvars.levels_extend == 'max':
# cmap.set_over(plotvars.cs[-1])
# cols = cols[:-1]
# Get colour scale for use in contouring
# If colour bar extensions are enabled then the colour map goes
# from 1 to ncols-2. The colours for the colour bar extensions
# are then changed on the colorbar and plot after the plot is made
if single_fill_color is None:
colmap = cscale_get_map()
cmap = matplotlib.colors.ListedColormap(colmap)
if plotvars.levels_extend in ['min', 'both']:
cmap.set_under(plotvars.cs[0])
if plotvars.levels_extend in ['max', 'both']:
cmap.set_over(plotvars.cs[-1])
else:
cols = single_fill_color
cmap = matplotlib.colors.ListedColormap(cols)
# Colour array for storing the cell colour. Start with -1 as the default
# as the colours run from 0 to np.size(levels)-1
colarr = np.zeros([np.shape(field)[0], np.shape(field)[1]])-1
for i in np.arange(np.size(levels)-1):
lev = levels[i]
pts = np.where(np.logical_and(field >= lev, field < levels[i+1]))
colarr[pts] = int(i)
# Change points that are masked back to -1
if isinstance(field, np.ma.MaskedArray):
pts = np.ma.where(field.mask)
if np.size(pts) > 0:
colarr[pts] = -1
norm = matplotlib.colors.BoundaryNorm(levels, cmap.N)
if isinstance(f, cf.Field):
if f.ref('grid_mapping_name:transverse_mercator', default=False):
lonlat = True
# Case of transverse mercator of which UKCP is an example
ref = f.ref('grid_mapping_name:transverse_mercator')
false_easting = ref['false_easting']
false_northing = ref['false_northing']
central_longitude = ref['longitude_of_central_meridian']
central_latitude = ref['latitude_of_projection_origin']
scale_factor = ref['scale_factor_at_central_meridian']
transform = ccrs.TransverseMercator(false_easting=false_easting,
false_northing=false_northing,
central_longitude=central_longitude,
central_latitude=central_latitude,
scale_factor=scale_factor)
# Extract the axes and data
xpts = np.append(f.dim('X').bounds.array[:, 0], f.dim('X').bounds.array[-1, 1])
ypts = np.append(f.dim('Y').bounds.array[:, 0], f.dim('Y').bounds.array[-1, 1])
field = np.squeeze(f.array)
plotargs = {'transform': transform}
else:
if two_d is False:
if bound:
xpts = x
ypts = y
else:
# Find x box boundaries
xpts = x[0] - (x[1] - x[0]) / 2.0
for ix in np.arange(np.size(x) - 1):
xpts = np.append(xpts, x[ix] + (x[ix + 1] - x[ix]) / 2.0)
xpts = np.append(xpts, x[ix + 1] + (x[ix + 1] - x[ix]) / 2.0)
# Find y box boundaries
ypts = y[0] - (y[1] - y[0]) / 2.0
for iy in np.arange(np.size(y) - 1):
ypts = np.append(ypts, y[iy] + (y[iy + 1] - y[iy]) / 2.0)
ypts = np.append(ypts, y[iy + 1] + (y[iy + 1] - y[iy]) / 2.0)
# Shift lon grid if needed
if lonlat:
# Extract upper bound and original rhs of box longitude bounding points
upper_bound = ypts[-1]
# Reduce xpts and ypts by 1 or shifting of grid fails
# The last points are the right / upper bounds for the last data box
xpts = xpts[0:-1]
ypts = ypts[0:-1]
if plotvars.lonmin < np.nanmin(xpts):
xpts = xpts - 360
if plotvars.lonmin > np.nanmax(xpts):
xpts = xpts + 360
# Add cyclic information if missing.
lonrange = np.nanmax(xpts) - np.nanmin(xpts)
if lonrange < 360 and lonrange > 350:
# field, xpts = cartopy_util.add_cyclic_point(field, xpts)
field, xpts = add_cyclic(field, xpts)
right_bound = xpts[-1] + (xpts[-1] - xpts[-2])
# Add end x and y end points
xpts = np.append(xpts, right_bound)
ypts = np.append(ypts, upper_bound)
if two_d:
# 2D lons and lats code
if fast:
xpts = x
ypts = y
else:
nx = np.shape(x)[1]
ny = np.shape(x)[0]
for ix in np.arange(nx):
for iy in np.arange(ny):
# Calculate the local size difference and set the square points
if ix < nx -2:
xdiff = (x[iy, ix+1] - x[iy, ix]) / 2
else:
xdiff = (x[iy, ix] - x[iy, ix-1]) / 2
if iy < ny - 2:
ydiff = (y[iy+1, ix] - y[iy, ix]) / 2
else:
ydiff = (y[iy, ix] - y[iy-1, ix]) / 2
xpts = [x[iy,ix]-xdiff, x[iy,ix]+xdiff, x[iy,ix]+xdiff, x[iy,ix]-xdiff, x[iy,ix]-xdiff]
ypts = [y[iy,ix]-ydiff, y[iy,ix]-ydiff, y[iy,ix]+ydiff, y[iy,ix]+ydiff, y[iy,ix]-ydiff]
# Plot the square
plotvars.mymap.add_patch(mpatches.Polygon(\
[[xpts[0], ypts[0]], [xpts[1],ypts[1]], [xpts[2], ypts[2]],\
[xpts[3],ypts[3]], [xpts[4], ypts[4]]],\
facecolor=plotvars.cs[int(colarr[iy,ix])], zorder=zorder,\
transform=ccrs.PlateCarree()))
return
# Polar stereographic
# Set points past plotting limb to be plotvars.boundinglat
# Also set any lats past the pole to be the pole
if plotvars.proj == 'npstere':
pts = np.where(ypts < plotvars.boundinglat)
if np.size(pts) > 0:
ypts[pts] = plotvars.boundinglat
pts = np.where(ypts > 90.0)
if np.size(pts) > 0:
ypts[pts] = 90.0
if plotvars.proj == 'spstere':
pts = np.where(ypts > plotvars.boundinglat)
if np.size(pts) > 0:
ypts[pts] = plotvars.boundinglat
pts = np.where(ypts < -90.0)
if np.size(pts) > 0:
ypts[pts] = -90.0
# Set the transform if not supplied to bfill
if transform:
lonlat = True
else:
transform = ccrs.PlateCarree()
if fast:
if type(clevs) == int:
norm = False
if two_d:
# Plot using pcolormesh if a 2D grid
#field = f
fixed_x = x.copy()
for i, start in enumerate(np.argmax(np.abs(np.diff(x)) > 180, axis=1)):
fixed_x[i, start+1:] += 360
plotvars.image = plotvars.mymap.pcolormesh(fixed_x, y, field, cmap=cmap, transform=transform, norm=norm)
else:
if lonlat:
for offset in [0, 360.0]:
if type(clevs) == int:
plotvars.image = plotvars.mymap.pcolormesh(xpts+offset, ypts, field, transform=transform, cmap=cmap)
else:
plotvars.image = plotvars.mymap.pcolormesh(xpts+offset, ypts, field, transform=transform, cmap=cmap, norm=norm)
else:
if type(clevs) == int:
plotvars.image = plotvars.plot.pcolormesh(xpts, ypts, field, cmap=cmap)
else:
plotvars.image = plotvars.plot.pcolormesh(xpts, ypts, field, cmap=cmap, norm=norm)
else:
if plotvars.plot_type == 1 and plotvars.proj != 'cyl':
for i in np.arange(np.size(levels)-1):
allverts = []
xy_stack = np.column_stack(np.where(colarr == i))
for pt in np.arange(np.shape(xy_stack)[0]):
ix = xy_stack[pt][1]
iy = xy_stack[pt][0]
lons = [xpts[ix], xpts[ix+1], xpts[ix+1], xpts[ix], xpts[ix]]
lats = [ypts[iy], ypts[iy], ypts[iy+1], ypts[iy+1], ypts[iy]]
txpts, typts = lons, lats
verts = [
(txpts[0], typts[0]),
(txpts[1], typts[1]),
(txpts[2], typts[2]),
(txpts[3], typts[3]),
(txpts[4], typts[4]),
]
allverts.append(verts)
# Make the collection and add it to the plot.
if single_fill_color is None:
color = plotvars.cs[i]
else:
color = single_fill_color
coll = PolyCollection(allverts, facecolor=color, edgecolors=color, alpha=alpha,
zorder=zorder, **plotargs)
if lonlat:
plotvars.mymap.add_collection(coll)
else:
plotvars.plot.add_collection(coll)
else:
for i in np.arange(np.size(levels)-1):
allverts = []
xy_stack = np.column_stack(np.where(colarr == i))
for pt in np.arange(np.shape(xy_stack)[0]):
ix = xy_stack[pt][1]
iy = xy_stack[pt][0]
verts = [
(xpts[ix], ypts[iy]),
(xpts[ix+1], ypts[iy]),
(xpts[ix+1], ypts[iy+1]),
(xpts[ix], ypts[iy+1]),
(xpts[ix], ypts[iy]),
]
allverts.append(verts)
# Make the collection and add it to the plot.
if single_fill_color is None:
color = plotvars.cs[i]
else:
color = single_fill_color
coll = PolyCollection(allverts, facecolor=color, edgecolors=color,
alpha=alpha, zorder=zorder, **plotargs)
if lonlat:
plotvars.mymap.add_collection(coll)
else:
plotvars.plot.add_collection(coll)
# Add white for undefined areas
if white:
allverts = []
xy_stack = np.column_stack(np.where(colarr == -1))
for pt in np.arange(np.shape(xy_stack)[0]):
ix = xy_stack[pt][1]
iy = xy_stack[pt][0]
verts = [
(xpts[ix], ypts[iy]),
(xpts[ix+1], ypts[iy]),
(xpts[ix+1], ypts[iy+1]),
(xpts[ix], ypts[iy+1]),
(xpts[ix], ypts[iy]),
]
allverts.append(verts)
# Make the collection and add it to the plot.
color = plotvars.cs[i]
coll = PolyCollection(allverts, facecolor='#ffffff', edgecolors='#ffffff',
alpha=alpha, zorder=zorder, **plotargs)
if lonlat:
plotvars.mymap.add_collection(coll)
else:
plotvars.plot.add_collection(coll)
def bfill_orig(f=None, x=None, y=None, clevs=False, lonlat=None, bound=False,
alpha=1.0, single_fill_color=None, white=True, zorder=4, fast=None, transform=False,
orca=False):
"""
| bfill - block fill a field with colour rectangles
| This is an internal routine and is not generally used by the user.
|
| f=None - field
| x=None - x points for field
| y=None - y points for field
| clevs=None - levels for filling
| lonlat=None - longitude and latitude data
| bound=False - x and y are cf data boundaries
| alpha=alpha - transparency setting 0 to 1
| white=True - colour unplotted areas white
| single_fill_color=None - colour for a blockfill between two levels
| - makes maplotlib named colours or
| - hexadecimal notation - '#d3d3d3' for grey
| zorder=4 - plotting order
| fast=None - use fast plotting with pcolormesh which is useful for larger datasets
| transform=False - map transform supplied by calling routine
| orca=False - data is orca data
|
:Returns:
None
|
|
|
|
"""
# Set lonlat if not specified
lonlat = False
if plotvars.plot_type == 1:
lonlat = True
# If single_fill_color is defined then turn off whiting out the background.
if single_fill_color is not None:
white = False
# Set 2D lon lat if data is that format
two_d = False
if not isinstance(f, cf.Field):
if np.ndim(x) == 2 and np.ndim(x) == 2:
two_d = True
# Set the default map coordinates for the data to be PlateCarree
plotargs = {}
if lonlat:
plotargs = {'transform': ccrs.PlateCarree()}
# Set the field
if isinstance(f, cf.Field):
field = f.array
else:
field = f
levels = np.array(deepcopy(clevs)).astype('float')
# Generate a Matplotlib colour map
if single_fill_color is None:
cols = plotvars.cs
else:
cols = single_fill_color
cmap = matplotlib.colors.ListedColormap(cols)
levels_orig = deepcopy(levels)
if single_fill_color is None:
if plotvars.levels_extend == 'both' or plotvars.levels_extend == 'min':
levels = np.insert(levels, 0, -1e30)
if plotvars.levels_extend == 'both' or plotvars.levels_extend == 'max':
levels = np.append(levels, 1e30)
if plotvars.levels_extend == 'both' or plotvars.levels_extend == 'min':
cmap.set_under(plotvars.cs[0])
cols = cols[1:]
if plotvars.levels_extend == 'both' or plotvars.levels_extend == 'max':
cmap.set_over(plotvars.cs[-1])
cols = cols[:-1]
# Colour array for storing the cell colour. Start with -1 as the default
# as the colours run from 0 to np.size(levels)-1
colarr = np.zeros([np.shape(field)[0], np.shape(field)[1]])-1
for i in np.arange(np.size(levels)-1):
lev = levels[i]
pts = np.where(np.logical_and(field >= lev, field < levels[i+1]))
colarr[pts] = int(i)
# Change points that are masked back to -1
if isinstance(field, np.ma.MaskedArray):
pts = np.ma.where(field.mask)
if np.size(pts) > 0:
colarr[pts] = -1
norm = matplotlib.colors.BoundaryNorm(levels, cmap.N)
if isinstance(f, cf.Field):
if f.ref('grid_mapping_name:transverse_mercator', default=False):
lonlat = True
# Case of transverse mercator of which UKCP is an example
ref = f.ref('grid_mapping_name:transverse_mercator')
false_easting = ref['false_easting']
false_northing = ref['false_northing']
central_longitude = ref['longitude_of_central_meridian']
central_latitude = ref['latitude_of_projection_origin']
scale_factor = ref['scale_factor_at_central_meridian']
transform = ccrs.TransverseMercator(false_easting=false_easting,
false_northing=false_northing,
central_longitude=central_longitude,
central_latitude=central_latitude,
scale_factor=scale_factor)
# Extract the axes and data
xpts = np.append(f.dim('X').bounds.array[:, 0], f.dim('X').bounds.array[-1, 1])
ypts = np.append(f.dim('Y').bounds.array[:, 0], f.dim('Y').bounds.array[-1, 1])
field = np.squeeze(f.array)
plotargs = {'transform': transform}
else:
if orca is False:
# Assign f to field as this may be modified in lat-lon plots
field = f
if two_d is False:
if bound:
xpts = x
ypts = y
else:
# Find x box boundaries
xpts = x[0] - (x[1] - x[0]) / 2.0
for ix in np.arange(np.size(x) - 1):
xpts = np.append(xpts, x[ix] + (x[ix + 1] - x[ix]) / 2.0)
xpts = np.append(xpts, x[ix + 1] + (x[ix + 1] - x[ix]) / 2.0)
# Find y box boundaries
ypts = y[0] - (y[1] - y[0]) / 2.0
for iy in np.arange(np.size(y) - 1):
ypts = np.append(ypts, y[iy] + (y[iy + 1] - y[iy]) / 2.0)
ypts = np.append(ypts, y[iy + 1] + (y[iy + 1] - y[iy]) / 2.0)
# Shift lon grid if needed
if lonlat:
# Extract upper bound and original rhs of box longitude bounding points
upper_bound = ypts[-1]
# Reduce xpts and ypts by 1 or shifting of grid fails
# The last points are the right / upper bounds for the last data box
xpts = xpts[0:-1]
ypts = ypts[0:-1]
if plotvars.lonmin < np.nanmin(xpts):
xpts = xpts - 360
if plotvars.lonmin > np.nanmax(xpts):
xpts = xpts + 360
# Add cyclic information if missing.
lonrange = np.nanmax(xpts) - np.nanmin(xpts)
if lonrange < 360 and lonrange > 350:
# field, xpts = cartopy_util.add_cyclic_point(field, xpts)
field, xpts = add_cyclic(field, xpts)
right_bound = xpts[-1] + (xpts[-1] - xpts[-2])
# Add end x and y end points
xpts = np.append(xpts, right_bound)
ypts = np.append(ypts, upper_bound)
else:
# 2D lons and lats code
nx = np.shape(x)[1]
ny = np.shape(x)[0]
for ix in np.arange(nx - 1):
for iy in np.arange(ny - 1):
# Calculate the local size difference and set the square points
if ix < nx -2:
xdiff = (x[iy, ix+1] - x[iy, ix]) / 2
else:
xdiff = (x[iy, ix] - x[iy, ix-1]) / 2
if iy < ny - 2:
ydiff = (y[iy+1, ix] - y[iy, ix]) / 2
else:
ydiff = (y[iy, ix] - y[iy-1, ix]) / 2
xpts = [x[iy,ix]-xdiff, x[iy,ix]+xdiff, x[iy,ix]+xdiff, x[iy,ix]-xdiff, x[iy,ix]-xdiff]
ypts = [y[iy,ix]-ydiff, y[iy,ix]-ydiff, y[iy,ix]+ydiff, y[iy,ix]+ydiff, y[iy,ix]-ydiff]
# Plot the square
plotvars.mymap.add_patch(mpatches.Polygon(\
[[xpts[0], ypts[0]], [xpts[1],ypts[1]], [xpts[2], ypts[2]],\
[xpts[3],ypts[3]], [xpts[4], ypts[4]]],\
facecolor=plotvars.cs[int(colarr[iy,ix])], zorder=zorder,\
transform=ccrs.PlateCarree()))
return
# Polar stereographic
# Set points past plotting limb to be plotvars.boundinglat
# Also set any lats past the pole to be the pole
if plotvars.proj == 'npstere' and not orca:
pts = np.where(ypts < plotvars.boundinglat)
if np.size(pts) > 0:
ypts[pts] = plotvars.boundinglat
pts = np.where(ypts > 90.0)
if np.size(pts) > 0:
ypts[pts] = 90.0
if plotvars.proj == 'spstere' and not orca:
pts = np.where(ypts > plotvars.boundinglat)
if np.size(pts) > 0:
ypts[pts] = plotvars.boundinglat
pts = np.where(ypts < -90.0)
if np.size(pts) > 0:
ypts[pts] = -90.0
# Set the transform if not supplied to bfill
if transform:
lonlat = True
else:
transform = ccrs.PlateCarree()
if fast:
if type(clevs) == int:
norm = False
if orca:
# Plot using pcolormesh if an orca grid
field = f
fixed_x = x.copy()
for i, start in enumerate(np.argmax(np.abs(np.diff(x)) > 180, axis=1)):
fixed_x[i, start+1:] += 360
plotvars.image = plotvars.mymap.pcolormesh(fixed_x, y, field, cmap=cmap, transform=transform)
else:
if lonlat:
for offset in [0, 360.0]:
if type(clevs) == int:
plotvars.image = plotvars.mymap.pcolormesh(xpts+offset, ypts, field, transform=transform, cmap=cmap)
else:
plotvars.image = plotvars.mymap.pcolormesh(xpts+offset, ypts, field, transform=transform, cmap=cmap, norm=norm)
else:
if type(clevs) == int:
plotvars.image = plotvars.plot.pcolormesh(xpts, ypts, field, cmap=cmap)
else:
plotvars.image = plotvars.plot.pcolormesh(xpts, ypts, field, cmap=cmap, norm=norm)
else:
if plotvars.plot_type == 1 and plotvars.proj != 'cyl':
for i in np.arange(np.size(levels)-1):
allverts = []
xy_stack = np.column_stack(np.where(colarr == i))
for pt in np.arange(np.shape(xy_stack)[0]):
ix = xy_stack[pt][1]
iy = xy_stack[pt][0]
lons = [xpts[ix], xpts[ix+1], xpts[ix+1], xpts[ix], xpts[ix]]
lats = [ypts[iy], ypts[iy], ypts[iy+1], ypts[iy+1], ypts[iy]]
txpts, typts = lons, lats
verts = [
(txpts[0], typts[0]),
(txpts[1], typts[1]),
(txpts[2], typts[2]),
(txpts[3], typts[3]),
(txpts[4], typts[4]),
]
allverts.append(verts)
# Make the collection and add it to the plot.
if single_fill_color is None:
color = plotvars.cs[i]
else:
color = single_fill_color
coll = PolyCollection(allverts, facecolor=color, edgecolors=color, alpha=alpha,
zorder=zorder, **plotargs)
if lonlat:
plotvars.mymap.add_collection(coll)
else:
plotvars.plot.add_collection(coll)
else:
for i in np.arange(np.size(levels)-1):
allverts = []
xy_stack = np.column_stack(np.where(colarr == i))
for pt in np.arange(np.shape(xy_stack)[0]):
ix = xy_stack[pt][1]
iy = xy_stack[pt][0]
verts = [
(xpts[ix], ypts[iy]),
(xpts[ix+1], ypts[iy]),
(xpts[ix+1], ypts[iy+1]),
(xpts[ix], ypts[iy+1]),
(xpts[ix], ypts[iy]),
]
allverts.append(verts)
# Make the collection and add it to the plot.
if single_fill_color is None:
color = plotvars.cs[i]
else:
color = single_fill_color
coll = PolyCollection(allverts, facecolor=color, edgecolors=color,
alpha=alpha, zorder=zorder, **plotargs)
if lonlat:
plotvars.mymap.add_collection(coll)
else:
plotvars.plot.add_collection(coll)
# Add white for undefined areas
if white:
allverts = []
xy_stack = np.column_stack(np.where(colarr == -1))
for pt in np.arange(np.shape(xy_stack)[0]):
ix = xy_stack[pt][1]
iy = xy_stack[pt][0]
verts = [
(xpts[ix], ypts[iy]),
(xpts[ix+1], ypts[iy]),
(xpts[ix+1], ypts[iy+1]),
(xpts[ix], ypts[iy+1]),
(xpts[ix], ypts[iy]),
]
allverts.append(verts)
# Make the collection and add it to the plot.
color = plotvars.cs[i]
coll = PolyCollection(allverts, facecolor='#ffffff', edgecolors='#ffffff',
alpha=alpha, zorder=zorder, **plotargs)
if lonlat:
plotvars.mymap.add_collection(coll)
else:
plotvars.plot.add_collection(coll)
[docs]
def regrid(f=None, x=None, y=None, xnew=None, ynew=None):
"""
| regrid - bilinear interpolation of a grid to new grid locations
|
|
| f=None - original field
| x=None - original field x values
| y=None - original field y values
| xnew=None - new x points
| ynew=None - new y points
|
:Returns:
field values at requested locations
|
|
"""
# Copy input arrays
regrid_f = deepcopy(f)
regrid_x = deepcopy(x)
regrid_y = deepcopy(y)
fieldout = []
# Reverse xpts and field if necessary
if regrid_x[0] > regrid_x[-1]:
regrid_x = regrid_x[::-1]
regrid_f = np.fliplr(regrid_f)
# Reverse ypts and field if necessary
if regrid_y[0] > regrid_y[-1]:
regrid_y = regrid_y[::-1]
regrid_f = np.flipud(regrid_f)
# Iterate over the new grid to get the new grid values.
for i in np.arange(np.size(xnew)):
xval = xnew[i]
yval = ynew[i]
# Find position of new grid point in the x and y arrays
myxpos = find_pos_in_array(vals=regrid_x, val=xval)
myypos = find_pos_in_array(vals=regrid_y, val=yval)
myxpos2 = myxpos + 1
myypos2 = myypos + 1
if (myxpos2 != myxpos):
alpha = (xnew[i] - regrid_x[myxpos]) / \
(regrid_x[myxpos2] - regrid_x[myxpos])
else:
alpha = (xnew[i] - regrid_x[myxpos]) / 1E-30
newval1 = (regrid_f[myypos, myxpos] - regrid_f[myypos, myxpos2])
newval1 = newval1 * alpha
newval1 = regrid_f[myypos, myxpos] - newval1
newval2 = (regrid_f[myypos2, myxpos] - regrid_f[myypos2, myxpos2])
newval2 = newval2 * alpha
newval2 = regrid_f[myypos2, myxpos] - newval2
if (myypos2 != myypos):
alpha2 = (ynew[i] - regrid_y[myypos])
alpha2 = alpha2 / (regrid_y[myypos2] - regrid_y[myypos])
else:
alpha2 = (ynew[i] - regrid_y[myypos]) / 1E-30
newval3 = newval1 - (newval1 - newval2) * alpha2
fieldout = np.append(fieldout, newval3)
return fieldout
[docs]
def stipple(f=None, x=None, y=None, min=None, max=None,
size=80, color='k', pts=50, marker='.', edgecolors='k',
alpha=1.0, ylog=False, zorder=1):
"""
| stipple - put markers on a plot to indicate value of interest
|
| f=None - cf field or field
| x=None - x points for field
| y=None - y points for field
| min=None - minimum threshold for stipple
| max=None - maximum threshold for stipple
| size=80 - default size for stipples
| color='k' - default colour for stipples
| pts=50 - number of points in the x direction
| marker='.' - default marker for stipples
| edegecolors='k' - outline colour
| alpha=1.0 - transparency setting - default is off
| ylog=False - set to True if a log pressure stipple plot
| is required
| zorder=2 - plotting order
|
|
:Returns:
None
|
|
"""
if plotvars.plot_type not in [1, 2, 3]:
errstr = '\n stipple error - only X-Y, X-Z and Y-Z \n'
errstr = errstr + 'stipple supported at the present time\n'
errstr = errstr + 'Please raise a feature request if you see this error.\n'
raise Warning(errstr)
# Extract required data for contouring
# If a cf-python field
if isinstance(f, cf.Field):
colorbar_title = ''
field, xpts, ypts, ptype, colorbar_title, xlabel, ylabel, xpole, \
ypole = cf_data_assign(f, colorbar_title)
elif isinstance(f, cf.FieldList):
raise TypeError("Can't plot a field list")
else:
field = f # field data passed in as f
check_data(field, x, y)
xpts = x
ypts = y
if plotvars.plot_type == 1:
# Cylindrical projection
# Add cyclic information if missing.
lonrange = np.nanmax(xpts) - np.nanmin(xpts)
if lonrange < 360:
# field, xpts = cartopy_util.add_cyclic_point(field, xpts)
field, xpts = add_cyclic(field, xpts)
#if plotvars.proj == 'cyl':
if plotvars.proj in ['cyl', 'robin', 'merc', 'ortho', 'moll']:
# Calculate interpolation points
xnew, ynew = stipple_points(xmin=np.nanmin(xpts),
xmax=np.nanmax(xpts),
ymin=np.nanmin(ypts),
ymax=np.nanmax(ypts),
pts=pts, stype=2)
# Calculate points in map space
xnew_map = xnew
ynew_map = ynew
if plotvars.proj == 'npstere' or plotvars.proj == 'spstere':
# Calculate interpolation points
xnew, ynew, xnew_map, ynew_map = polar_regular_grid()
# Convert longitudes to be 0 to 360
# negative longitudes are incorrectly regridded in polar stereographic projection
xnew = np.mod(xnew + 360.0, 360.0)
if plotvars.plot_type >= 2 and plotvars.plot_type <= 3:
# Flip data if a lat-height plot and lats start at the north pole
if plotvars.plot_type == 2:
if xpts[0] > xpts[-1]:
xpts = xpts[::-1]
field = np.fliplr(field)
# Calculate interpolation points
ymin = np.nanmin(ypts)
ymax = np.nanmax(ypts)
if ylog:
ymin = np.log10(ymin)
ymax = np.log10(ymax)
xnew, ynew = stipple_points(xmin=np.nanmin(xpts),
xmax=np.nanmax(xpts),
ymin=ymin,
ymax=ymax,
pts=pts, stype=2)
if ylog:
ynew = 10**ynew
# Get values at the new points
vals = regrid(f=field, x=xpts, y=ypts, xnew=xnew, ynew=ynew)
# Work out which of the points are valid
valid_points = np.array([], dtype='int64')
for i in np.arange(np.size(vals)):
if vals[i] >= min and vals[i] <= max:
valid_points = np.append(valid_points, i)
if plotvars.plot_type == 1:
proj = ccrs.PlateCarree()
if np.size(valid_points) > 0:
plotvars.mymap.scatter(xnew[valid_points], ynew[valid_points],
s=size, c=color, marker=marker,
edgecolors=edgecolors,
alpha=alpha, transform=proj, zorder=zorder)
if plotvars.plot_type >= 2 and plotvars.plot_type <= 3:
plotvars.plot.scatter(xnew[valid_points], ynew[valid_points],
s=size, c=color, marker=marker,
edgecolors=edgecolors,
alpha=alpha, zorder=zorder)
[docs]
def stipple_points(xmin=None, xmax=None, ymin=None,
ymax=None, pts=None, stype=None):
"""
| stipple_points - calculate interpolation points
|
| xmin=None - plot x minimum
| ymax=None - plot x maximum
| ymin=None - plot y minimum
| ymax=None - plot x maximum
| pts=None - number of points in the x and y directions
| one number gives the same in both directions
|
| stype=None - type of grid. 1=regular, 2=offset
|
|
|
:Returns:
stipple locations in x and y
|
|
"""
# Work out number of points in x and y directions
if np.size(pts) == 1:
pts_x = pts
pts_y = pts
if np.size(pts) == 2:
pts_x = pts[0]
pts_y = pts[1]
# Create regularly spaced points
xstep = (xmax - xmin) / float(pts_x)
x1 = [xmin + xstep / 4]
while (np.nanmax(x1) + xstep) < xmax - xstep / 10:
x1 = np.append(x1, np.nanmax(x1) + xstep)
x2 = [xmin + xstep * 3 / 4]
while (np.nanmax(x2) + xstep) < xmax - xstep / 10:
x2 = np.append(x2, np.nanmax(x2) + xstep)
ystep = (ymax - ymin) / float(pts_y)
y1 = [ymin + ystep / 2]
while (np.nanmax(y1) + ystep) < ymax - ystep / 10:
y1 = np.append(y1, np.nanmax(y1) + ystep)
# Create interpolation points
xnew = []
ynew = []
iy = 0
for y in y1:
iy = iy + 1
if stype == 1:
xnew = np.append(xnew, x1)
y2 = np.zeros(np.size(x1))
y2.fill(y)
ynew = np.append(ynew, y2)
if stype == 2:
if iy % 2 == 0:
xnew = np.append(xnew, x1)
y2 = np.zeros(np.size(x1))
y2.fill(y)
ynew = np.append(ynew, y2)
if iy % 2 == 1:
xnew = np.append(xnew, x2)
y2 = np.zeros(np.size(x2))
y2.fill(y)
ynew = np.append(ynew, y2)
return xnew, ynew
[docs]
def find_pos_in_array(vals=None, val=None, above=False):
"""
| find_pos_in_array - find the position of a point in an array
|
| vals - array values
| val - value to find position of
|
|
|
|
|
|
:Returns:
position in array
|
|
|
"""
pos = -1
if above is False:
for myval in vals:
if val > myval:
pos = pos + 1
if above:
for myval in vals:
if val >= myval:
pos = pos + 1
if np.size(vals) - 1 > pos:
pos = pos + 1
return pos
[docs]
def vect(u=None, v=None, x=None, y=None, scale=None, stride=None, pts=None,
key_length=None, key_label=None, ptype=None, title=None, magmin=None,
width=0.02, headwidth=3, headlength=5, headaxislength=4.5,
pivot='middle', key_location=[0.95, -0.06], key_show=True, axes=True,
xaxis=True, yaxis=True, xticks=None, xticklabels=None, yticks=None,
yticklabels=None, xlabel=None, ylabel=None, ylog=False, color='k',
zorder=3, titles=None, alpha=1.0):
"""
| vect - plot vectors
|
| u=None - u wind
| v=None - v wind
| x=None - x locations of u and v
| y=None - y locations of u and v
| scale=None - data units per arrow length unit. A smaller values gives
| a larger vector. Generally takes one value but in the case
| of two supplied values the second vector scaling applies to
| the v field.
| stride=None - plot vector every stride points. Can take two values one
| for x and one for y.
| pts=None - use bilinear interpolation to interpolate vectors onto a new
| grid - takes one or two values.
| If one value is passed then this is used for both the x and
| y axes.
| magmin=None - don't plot any vects with less than this magnitude.
| key_length=None - length of the key. Generally takes one value but in
| the case of two supplied values the second vector
| scaling applies to the v field.
| key_label=None - label for the key. Generally takes one value but in the
| case of two supplied values the second vector scaling
| applies to the v field.
| key_location=[0.9, -0.06] - location of the vector key relative to the
| plot in normalised coordinates.
| key_show=True - draw the key. Set to False if not required.
| ptype=0 - plot type - not needed for cf fields.
| 0 = no specific plot type,
| 1 = longitude-latitude,
| 2 = latitude - height,
| 3 = longitude - height,
| 4 = latitude - time,
| 5 = longitude - time
| 6 = rotated pole
|
| title=None - plot title
| width=0.005 - shaft width in arrow units; default is 0.005 times the
| width of the plot
| headwidth=3 - head width as multiple of shaft width, default is 3
| headlength=5 - head length as multiple of shaft width, default is 5
| headaxislength=4.5 - head length at shaft intersection, default is 4.5
| pivot='middle' - the part of the arrow that is at the grid point; the
| arrow rotates about this point
takes 'tail', 'middle', 'tip'
| axes=True - plot x and y axes
| xaxis=True - plot xaxis
| yaxis=True - plot y axis
| xticks=None - xtick positions
| xticklabels=None - xtick labels
| yticks=None - y tick positions
| yticklabels=None - ytick labels
| xlabel=None - label for x axis
| ylabel=None - label for y axis
| ylog=False - log y axis
| color='k' - colour for the vectors - default is black.
| zorder=3 - plotting order
| titles=None - generate dimension and cell_methods titles for plot
| alpha=1.0 - transparency setting. The default is no transparency.
|
:Returns:
None
|
|
|
"""
# If the vector color is white set the quicker key colour to black
# so that it can be seen
qkey_color = color
if qkey_color == 'w' or qkey_color == 'white':
qkey_color = 'k'
colorbar_title = ''
text_fontsize = plotvars.text_fontsize
continent_thickness = plotvars.continent_thickness
continent_color = plotvars.continent_color
if text_fontsize is None:
text_fontsize = 11
if continent_thickness is None:
continent_thickness = 1.5
if continent_color is None:
continent_color = 'k'
# ylog=plotvars.ylog
title_fontsize = plotvars.title_fontsize
title_fontweight = plotvars.title_fontweight
if title_fontsize is None:
title_fontsize = 15
resolution_orig = plotvars.resolution
# Set potential user axis labels
user_xlabel = xlabel
user_ylabel = ylabel
rotated_vect = False
if isinstance(u, cf.Field):
if u.ref('grid_mapping_name:rotated_latitude_longitude', default=False):
rotated_vect = True
# Extract required data
# If a cf-python field
if isinstance(u, cf.Field):
# Check data is 2D
ndims = np.squeeze(u.data).ndim
if ndims != 2:
errstr = "\n\ncfp.vect error need a 2 dimensonal u field to make vectors\n"
errstr += "received " + str(np.squeeze(u.data).ndim)
if ndims == 1:
errstr += " dimension\n\n"
else:
errstr += " dimensions\n\n"
raise TypeError(errstr)
u_data, u_x, u_y, ptype, colorbar_title, xlabel, ylabel, xpole, \
ypole = cf_data_assign(u, colorbar_title, rotated_vect=rotated_vect)
elif isinstance(u, cf.FieldList):
raise TypeError("Can't plot a field list")
else:
# field=f #field data passed in as f
check_data(u, x, y)
u_data = deepcopy(u)
u_x = deepcopy(x)
u_y = deepcopy(y)
xlabel = ''
ylabel = ''
if isinstance(v, cf.Field):
# Check data is 2D
ndims = np.squeeze(v.data).ndim
if ndims != 2:
errstr = "\n\ncfp.vect error need a 2 dimensonal v field to make vectors\n"
errstr += "received " + str(np.squeeze(v.data).ndim)
if ndims == 1:
errstr += " dimension\n\n"
else:
errstr += " dimensions\n\n"
raise TypeError(errstr)
v_data, v_x, v_y, ptype, colorbar_title, xlabel, ylabel, xpole, \
ypole = cf_data_assign(v, colorbar_title, rotated_vect=rotated_vect)
elif isinstance(v, cf.FieldList):
raise TypeError("Can't plot a field list")
else:
# field=f #field data passed in as f
check_data(v, x, y)
v_data = deepcopy(v)
v_x = deepcopy(x)
xlabel = ''
ylabel = ''
# If a minimum magnitude is specified mask these data points
if magmin is not None:
mag = np.sqrt(u_data**2 + v_data**2)
invalid = np.where(mag <= magmin)
if np.size(invalid) > 0:
u_data[invalid] = np.nan
v_data[invalid] = np.nan
# Reset xlabel and ylabel values with user defined labels in specified
if user_xlabel is not None:
xlabel = user_xlabel
if user_ylabel is not None:
ylabel = user_ylabel
# Retrieve any user defined axis labels
if xlabel == '' and plotvars.xlabel is not None:
xlabel = plotvars.xlabel
if ylabel == '' and plotvars.ylabel is not None:
ylabel = plotvars.ylabel
if xticks is None and plotvars.xticks is not None:
xticks = plotvars.xticks
if plotvars.xticklabels is not None:
xticklabels = plotvars.xticklabels
else:
xticklabels = list(map(str, xticks))
if yticks is None and plotvars.yticks is not None:
yticks = plotvars.yticks
if plotvars.yticklabels is not None:
yticklabels = plotvars.yticklabels
else:
yticklabels = list(map(str, yticks))
if scale is None:
scale = np.nanmax(u_data) / 4.0
if key_length is None:
key_length = scale
# Calculate a set of dimension titles if requested
if titles:
title_dims = generate_titles(u)
title_dims = 'u\n' + title_dims
title_dims2 = generate_titles(v)
title_dims2 = 'v\n' + title_dims2
# Open a new plot if necessary
if plotvars.user_plot == 0:
gopen(user_plot=0)
# Call gpos(1) if not already called
if plotvars.rows > 1 or plotvars.columns > 1:
if plotvars.gpos_called is False:
gpos(1)
# Set plot type if user specified
if (ptype is not None):
plotvars.plot_type = ptype
lonrange = np.nanmax(u_x) - np.nanmin(u_x)
latrange = np.nanmax(u_y) - np.nanmin(u_y)
if plotvars.plot_type == 1:
# Set up mapping
if (lonrange > 350 and latrange > 170) or plotvars.user_mapset == 1:
set_map()
else:
mapset(lonmin=np.nanmin(u_x), lonmax=np.nanmax(u_x),
latmin=np.nanmin(u_y), latmax=np.nanmax(u_y),
user_mapset=0, resolution=resolution_orig)
set_map()
mymap = plotvars.mymap
# u_data, u_x = cartopy_util.add_cyclic_point(u_data, u_x)
u_data, u_x = add_cyclic(u_data, u_x)
# v_data, v_x = cartopy_util.add_cyclic_point(v_data, v_x)
v_data, v_x = add_cyclic(v_data, v_x)
# stride data points to reduce vector density
if stride is not None:
if np.size(stride) == 1:
xstride = stride
ystride = stride
if np.size(stride) == 2:
xstride = stride[0]
ystride = stride[1]
u_x = u_x[0::xstride]
u_y = u_y[0::ystride]
u_data = u_data[0::ystride, 0::xstride]
v_data = v_data[0::ystride, 0::xstride]
# Map vectors
if plotvars.plot_type == 1:
lonmax = plotvars.lonmax
proj = ccrs.PlateCarree()
# Fix for high latitude vectors as described at https://github.com/SciTools/cartopy/issues/1179
if plotvars.proj != 'cyl':
u_src_crs = u_data / np.cos(u_y[:, np.newaxis] / 180 * np.pi)
v_src_crs = v_data
magnitude = np.ma.sqrt(u_data**2 + v_data**2)
magn_src_crs = np.ma.sqrt(u_src_crs**2 + v_src_crs**2)
u_data = u_src_crs * magnitude / magn_src_crs
v_data = v_src_crs * magnitude / magn_src_crs
if pts is None:
quiv = plotvars.mymap.quiver(u_x, u_y, u_data, v_data, scale=scale,
pivot=pivot, units='inches',
width=width, headwidth=headwidth,
headlength=headlength,
headaxislength=headaxislength,
color=color, transform=proj,
alpha=alpha, zorder=zorder)
else:
if plotvars.proj == 'cyl':
# **cartopy 0.16 fix for longitide points in cylindrical projection
# when regridding to a number of points
# Make points within the plotting region
for pt in np.arange(np.size(u_x)):
if u_x[pt] > lonmax:
u_x[pt] = u_x[pt]-360
quiv = plotvars.mymap.quiver(u_x, u_y, u_data, v_data, scale=scale,
pivot=pivot, units='inches',
width=width, headwidth=headwidth,
headlength=headlength,
headaxislength=headaxislength,
color=color,
regrid_shape=pts, transform=proj,
alpha=alpha, zorder=zorder)
# Make key_label if none exists
if key_label is None:
key_label = str(key_length)
if isinstance(u, cf.Field):
key_label = supscr(key_label + u.units)
if key_show:
plotvars.mymap.quiverkey(quiv, key_location[0],
key_location[1],
key_length,
key_label, labelpos='W',
color=qkey_color,
fontproperties={'size': str(plotvars.axis_label_fontsize)},
coordinates='axes')
# axes
plot_map_axes(axes=axes, xaxis=xaxis, yaxis=yaxis,
xticks=xticks, xticklabels=xticklabels,
yticks=yticks, yticklabels=yticklabels,
user_xlabel=user_xlabel, user_ylabel=user_ylabel,
verbose=False)
# Coastlines
continent_thickness = plotvars.continent_thickness
continent_color = plotvars.continent_color
continent_linestyle = plotvars.continent_linestyle
if continent_thickness is None:
continent_thickness = 1.5
if continent_color is None:
continent_color = 'k'
if continent_linestyle is None:
continent_linestyle = 'solid'
feature = cfeature.NaturalEarthFeature(name='land', category='physical',
scale=plotvars.resolution,
facecolor='none')
mymap.add_feature(feature, edgecolor=continent_color,
linewidth=continent_thickness,
linestyle=continent_linestyle)
# Title
if title is not None:
map_title(title)
# Titles for dimensions
if titles:
if plotvars.titles_con_called is False:
dim_titles(title=title_dims, title2=title_dims2)
else:
dim_titles(title2=title_dims, title3=title_dims2)
if plotvars.plot_type == 6:
if u.ref('grid_mapping_name:rotated_latitude_longitude', False):
proj = ccrs.PlateCarree()
# Set up mapping
if (lonrange > 350 and latrange > 170) or plotvars.user_mapset == 1:
set_map()
else:
mapset(lonmin=np.nanmin(u_x), lonmax=np.nanmax(u_x),
latmin=np.nanmin(u_y), latmax=np.nanmax(u_y),
user_mapset=0, resolution=resolution_orig)
set_map()
quiv = plotvars.mymap.quiver(u_x, u_y, u_data, v_data, scale=scale*10, transform=proj,
pivot=pivot, units='inches',
width=width, headwidth=headwidth,
headlength=headlength,
headaxislength=headaxislength,
color=color, alpha=alpha, zorder=zorder)
# Make key_label if none exists
if key_label is None:
key_label = str(key_length)
if isinstance(u, cf.Field):
key_label = supscr(key_label + u.units)
if key_show:
plotvars.mymap.quiverkey(quiv, key_location[0],
key_location[1],
key_length,
key_label, labelpos='W',
color=qkey_color,
fontproperties={'size': str(plotvars.axis_label_fontsize)},
coordinates='axes')
# Axes on the native grid
if plotvars.plot == 'rotated':
rgaxes(xpole=xpole, ypole=ypole, xvec=x, yvec=y,
xticks=xticks, xticklabels=xticklabels,
yticks=yticks, yticklabels=yticklabels,
axes=axes, xaxis=xaxis, yaxis=yaxis,
xlabel=xlabel, ylabel=ylabel)
if plotvars.plot == 'cyl':
plot_map_axes(axes=axes, xaxis=xaxis, yaxis=yaxis,
xticks=xticks, xticklabels=xticklabels,
yticks=yticks, yticklabels=yticklabels,
user_xlabel=user_xlabel, user_ylabel=user_ylabel,
verbose=False)
# Title
if title is not None:
map_title(title)
# Titles for dimensions
if titles:
dim_titles(title=title_dims, titles2=dim_titles2)
######################################
# Latitude or longitude vs height plot
######################################
if plotvars.plot_type == 2 or plotvars.plot_type == 3:
user_gset = plotvars.user_gset
if user_gset == 0:
# Program selected data plot limits
xmin = np.nanmin(u_x)
xmax = np.nanmax(u_x)
if plotvars.plot_type == 2:
if xmin < -80 and xmin >= -90:
xmin = -90
if xmax > 80 and xmax <= 90:
xmax = 90
ymin = np.nanmin(u_y)
if ymin <= 10:
ymin = 0
ymax = np.nanmax(u_y)
else:
# User specified plot limits
xmin = plotvars.xmin
xmax = plotvars.xmax
if plotvars.ymin < plotvars.ymax:
ymin = plotvars.ymin
ymax = plotvars.ymax
else:
ymin = plotvars.ymax
ymax = plotvars.ymin
ystep = None
if (ymax == 1000):
ystep = 100
if (ymax == 100000):
ystep = 10000
ytype = 0 # pressure or similar y axis
if 'theta' in ylabel.split(' '):
ytype = 1
if 'height' in ylabel.split(' '):
ytype = 1
ystep = 100
if (ymax - ymin) > 5000:
ystep = 500.0
if (ymax - ymin) > 10000:
ystep = 1000.0
if (ymax - ymin) > 50000:
ystep = 10000.0
# Set plot limits and draw axes
if ylog != 1:
if ytype == 1:
gset(
xmin=xmin,
xmax=xmax,
ymin=ymin,
ymax=ymax,
user_gset=user_gset)
else:
gset(
xmin=xmin,
xmax=xmax,
ymin=ymax,
ymax=ymin,
user_gset=user_gset)
# Set default x-axis labels
lltype = 1
if plotvars.plot_type == 2:
lltype = 2
llticks, lllabels = mapaxis(min=xmin, max=xmax, type=lltype)
heightticks = gvals(
dmin=ymin,
dmax=ymax,
mystep=ystep,
mod=False)[0]
heightlabels = heightticks
if axes:
if xaxis:
if xticks is not None:
llticks = xticks
lllabels = xticks
if xticklabels is not None:
lllabels = xticklabels
else:
llticks = [100000000]
xlabel = ''
if yaxis:
if yticks is not None:
heightticks = yticks
heightlabels = yticks
if yticklabels is not None:
heightlabels = yticklabels
else:
heightticks = [100000000]
ylabel = ''
else:
llticks = [100000000]
heightticks = [100000000]
xlabel = ''
ylabel = ''
axes_plot(xticks=llticks, xticklabels=lllabels,
yticks=heightticks, yticklabels=heightlabels,
xlabel=xlabel, ylabel=ylabel)
# Log y axis
if ylog:
if ymin == 0:
ymin = 1 # reset zero mb/height input to a small value
gset(xmin=xmin,
xmax=xmax,
ymin=ymax,
ymax=ymin,
ylog=1,
user_gset=user_gset)
llticks, lllabels = mapaxis(min=xmin,
max=xmax,
type=plotvars.plot_type)
if axes:
if xaxis:
if xticks is not None:
llticks = xticks
lllabels = xticks
if xticklabels is not None:
lllabels = xticklabels
else:
llticks = [100000000]
xlabel = ''
if yaxis:
if yticks is not None:
heightticks = yticks
heightlabels = yticks
if yticklabels is not None:
heightlabels = yticklabels
else:
heightticks = [100000000]
ylabel = ''
if yticks is None:
axes_plot(
xticks=llticks,
xticklabels=lllabels,
xlabel=xlabel,
ylabel=ylabel)
else:
axes_plot(xticks=llticks, xticklabels=lllabels,
yticks=heightticks, yticklabels=heightlabels,
xlabel=xlabel, ylabel=ylabel)
# Regrid the data if requested
if pts is not None:
xnew, ynew = stipple_points(xmin=np.min(u_x), xmax=np.max(u_x),
ymin=np.min(u_y), ymax=np.max(u_y),
pts=pts, stype=1)
if ytype == 0:
# Make y interpolation in log space as we have a pressure coordinate
u_vals = regrid(f=u_data, x=u_x, y=np.log10(u_y), xnew=xnew, ynew=np.log10(ynew))
v_vals = regrid(f=v_data, x=u_x, y=np.log10(u_y), xnew=xnew, ynew=np.log10(ynew))
else:
u_vals = regrid(f=u_data, x=u_x, y=u_y, xnew=xnew, ynew=ynew)
v_vals = regrid(f=v_data, x=u_x, y=u_y, xnew=xnew, ynew=ynew)
u_x = xnew
u_y = ynew
u_data = u_vals
v_data = v_vals
# set scale and key lengths
if np.size(scale) == 1:
scale_u = scale
scale_v = scale
else:
scale_u = scale[0]
scale_v = scale[1]
if np.size(key_length) == 2:
key_length_u = key_length[0]
key_length_v = key_length[1]
# scale v data
v_data = v_data * scale_u / scale_v
else:
key_length_u = key_length
# Plot the vectors
quiv = plotvars.plot.quiver(u_x, u_y, u_data, v_data, pivot=pivot,
units='inches', scale=scale_u,
width=width, headwidth=headwidth,
headlength=headlength,
headaxislength=headaxislength,
color=color, alpha=alpha, zorder=zorder)
# Plot single key
if np.size(scale) == 1:
# Single scale vector
if key_label is None:
key_label_u = str(key_length_u)
if isinstance(u, cf.Field):
key_label_u = supscr(key_label_u + ' (' + u.units + ')')
else:
key_label_u = key_label[0]
if key_show:
plotvars.plot.quiverkey(quiv, key_location[0],
key_location[1],
key_length_u, key_label_u,
labelpos='W',
color=qkey_color,
fontproperties={'size': str(plotvars.axis_label_fontsize)})
# Plot two keys
if np.size(scale) == 2:
# translate from normalised units to plot units
xpos = key_location[0] * \
(plotvars.xmax - plotvars.xmin) + plotvars.xmin
ypos = key_location[1] * \
(plotvars.ymax - plotvars.ymin) + plotvars.ymin
# horizontal and vertical spacings for offsetting vector reference
# text
xoffset = 0.01 * abs(plotvars.xmax - plotvars.xmin)
yoffset = 0.01 * abs(plotvars.ymax - plotvars.ymin)
# Assign key labels if necessary
if key_label is None:
key_label_u = str(key_length_u)
key_label_v = str(key_length_v)
if isinstance(u, cf.Field):
key_label_u = supscr(key_label_u + ' (' + u.units + ')')
if isinstance(v, cf.Field):
key_label_v = supscr(key_label_v + ' (' + v.units + ')')
else:
key_label_u = supscr(key_label[0])
key_label_v = supscr(key_label[1])
# Plot reference vectors and keys
if key_show:
plotvars.plot.quiver(xpos, ypos, key_length[0], 0,
pivot='tail', units='inches',
scale=scale[0],
headaxislength=headaxislength,
width=width, headwidth=headwidth,
headlength=headlength,
clip_on=False,
color=qkey_color)
plotvars.plot.quiver(xpos, ypos, 0, key_length[1],
pivot='tail', units='inches',
scale=scale[1],
headaxislength=headaxislength,
width=width, headwidth=headwidth,
headlength=headlength,
clip_on=False,
color=qkey_color)
plotvars.plot.text(xpos,
ypos + yoffset,
key_label_u,
horizontalalignment='left',
verticalalignment='top')
plotvars.plot.text(xpos - xoffset,
ypos,
key_label_v,
horizontalalignment='right',
verticalalignment='bottom')
if title is not None:
plotvars.plot.set_title(title,
y=1.03,
fontsize=plotvars.title_fontsize,
fontweight=title_fontweight)
# Titles for dimensions
if titles:
dim_titles(title=title_dims, titles2=dim_titles2)
##########
# Save plot
##########
if plotvars.user_plot == 0:
gset()
cscale()
gclose()
if plotvars.user_mapset == 0:
mapset()
mapset(resolution=resolution_orig)
[docs]
def set_map():
"""
| set_map - set map and write into plotvars.mymap
|
| No inputs
| This is an internal routine and not used by the user
|
|
|
|
|
:Returns:
None
|
|
|
"""
# Return if mymap is already set
if plotvars.mymap is not None:
return
# Set up mapping
extent = True
lon_mid = plotvars.lonmin + (plotvars.lonmax - plotvars.lonmin) / 2.0
lonmin = plotvars.lonmin
lonmax = plotvars.lonmax
latmin = plotvars.latmin
latmax = plotvars.latmax
if plotvars.proj == 'cyl':
proj = ccrs.PlateCarree(central_longitude=lon_mid)
# Cartopy line plotting and identical left == right fix
if lonmax - lonmin == 360.0:
lonmax = lonmax + 0.01
if plotvars.proj == 'merc':
min_latitude = -80.0
if plotvars.lonmin > min_latitude:
min_latitude = plotvars.lonmin
max_latitude = 84.0
if plotvars.lonmax < max_latitude:
max_latitude = plotvars.lonmax
proj = ccrs.Mercator(central_longitude=plotvars.lon_0,
min_latitude=min_latitude,
max_latitude=max_latitude)
if plotvars.proj == 'npstere':
proj = ccrs.NorthPolarStereo(central_longitude=plotvars.lon_0)
# **cartopy 0.16 fix
# Here we add in 0.01 to the longitude extent as this helps with plotting
# lines and line labels
lonmin = plotvars.lon_0-180
lonmax = plotvars.lon_0+180.01
latmin = plotvars.boundinglat
latmax = 90
if plotvars.proj == 'spstere':
proj = ccrs.SouthPolarStereo(central_longitude=plotvars.lon_0)
# **cartopy 0.16 fix
# Here we add in 0.01 to the longitude extent as this helps with plotting
# lines and line labels
lonmin = plotvars.lon_0-180
lonmax = plotvars.lonmax+180.01
latmin = -90
latmax = plotvars.boundinglat
if plotvars.proj == 'ortho':
proj = ccrs.Orthographic(central_longitude=plotvars.lon_0,
central_latitude=plotvars.lat_0)
lonmin = plotvars.lon_0-180.0
lonmax = plotvars.lon_0+180.01
extent = False
if plotvars.proj == 'moll':
proj = ccrs.Mollweide(central_longitude=plotvars.lon_0)
lonmin = plotvars.lon_0-180.0
lonmax = plotvars.lon_0+180.01
extent = False
if plotvars.proj == 'robin':
proj = ccrs.Robinson(central_longitude=plotvars.lon_0)
if plotvars.proj == 'lcc':
latmin = plotvars.latmin
latmax = plotvars.latmax
lonmin = plotvars.lonmin
lonmax = plotvars.lonmax
lon_0 = lonmin+(lonmax-lonmin)/2.0
lat_0 = latmin+(latmax-latmin)/2.0
cutoff = -40
if lat_0 <= 0:
cutoff = 40
standard_parallels = [33, 45]
if latmin <= 0 and latmax <= 0:
standard_parallels = [-45, -33]
proj = ccrs.LambertConformal(central_longitude=lon_0,
central_latitude=lat_0,
cutoff=cutoff, standard_parallels=standard_parallels)
if plotvars.proj == 'rotated':
proj = ccrs.PlateCarree(central_longitude=lon_mid)
if plotvars.proj == 'OSGB':
proj = ccrs.OSGB()
if plotvars.proj == 'EuroPP':
proj = ccrs.EuroPP()
if plotvars.proj == 'UKCP':
# Special case of TransverseMercator for UKCP
proj = ccrs.TransverseMercator()
if plotvars.proj == 'TransverseMercator':
proj = ccrs.TransverseMercator()
lonmin = plotvars.lon_0-180.0
lonmax = plotvars.lon_0+180.01
extent = False
if plotvars.proj == 'LambertCylindrical':
proj = ccrs.LambertCylindrical()
lonmin = plotvars.lonmin
lonmax = plotvars.lonmax
latmin = plotvars.latmin
latmax = plotvars.latmax
extent = True
# Add a plot containing the projection
if plotvars.plot_xmin:
delta_x = plotvars.plot_xmax - plotvars.plot_xmin
delta_y = plotvars.plot_ymax - plotvars.plot_ymin
mymap = plotvars.master_plot.add_axes([plotvars.plot_xmin,
plotvars.plot_ymin,
delta_x, delta_y],
projection=proj)
else:
mymap = plotvars.master_plot.add_subplot(plotvars.rows,
plotvars.columns,
plotvars.pos,
projection=proj)
# Set map extent
set_extent = True
if plotvars.proj in ['OSGB', 'EuroPP', 'UKCP', 'robin', 'lcc']:
set_extent = False
if extent and set_extent:
mymap.set_extent([lonmin, lonmax, latmin, latmax], crs=ccrs.PlateCarree())
# Set the scaling for PlateCarree
if plotvars.proj == 'cyl':
mymap.set_aspect(plotvars.aspect)
if plotvars.proj == 'lcc':
# Special case of lcc
mymap.set_extent([lonmin, lonmax, latmin, latmax], crs=ccrs.PlateCarree())
if plotvars.proj == 'UKCP':
# Special case of TransverseMercator for UKCP
mymap.set_extent([-11, 3, 49, 61], crs=ccrs.PlateCarree())
if plotvars.proj == 'EuroPP':
# EuroPP somehow needs some limits setting.
mymap.set_extent([-12, 25, 30, 75], crs=ccrs.PlateCarree())
# Remove any plotvars.plot axes leaving just the plotvars.mymap axes
plotvars.plot.set_frame_on(False)
plotvars.plot.set_xticks([])
plotvars.plot.set_yticks([])
# Store map
plotvars.mymap = mymap
[docs]
def polar_regular_grid(pts=50):
"""
| polar_regular_grid - return a regular grid over a polar
| stereographic area
|
| pts=50 - number of grid points in the x and y directions
|
|
|
|
|
|
:Returns:
lons, lats of grid in degrees
x, y locations of lons and lats
|
|
|
"""
boundinglat = plotvars.boundinglat
lon_0 = plotvars.lon_0
if plotvars.proj == 'npstere':
thisproj = ccrs.NorthPolarStereo(central_longitude=lon_0)
else:
thisproj = ccrs.SouthPolarStereo(central_longitude=lon_0)
# Find min and max of plotting region in device coordinates
lons = np.array([lon_0-90, lon_0, lon_0+90, lon_0+180])
lats = np.array([boundinglat, boundinglat, boundinglat, boundinglat])
extent = thisproj.transform_points(ccrs.PlateCarree(), lons, lats)
xmin = np.min(extent[:, 0])
xmax = np.max(extent[:, 0])
ymin = np.min(extent[:, 1])
ymax = np.max(extent[:, 1])
# Make up a stipple of points for cover the pole
points_device = stipple_points(
xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, pts=pts, stype=2)
xnew = np.array(points_device)[0, :]
ynew = np.array(points_device)[1, :]
points_polar = ccrs.PlateCarree().transform_points(thisproj, xnew, ynew)
lons = np.array(points_polar)[:, 0]
lats = np.array(points_polar)[:, 1]
if plotvars.proj == 'npstere':
valid = np.where(lats >= boundinglat)
else:
valid = np.where(lats <= boundinglat)
return lons[valid], lats[valid], xnew[valid], ynew[valid]
[docs]
def cf_var_name(field=None, dim=None):
"""
| cf_var_name - return the name from a supplied dimension
| in the following order
| ncvar
| short_name
| long_name
| standard_name
|
| field=None - field
| dim=None - dimension required - 'dim0', 'dim1' etc.
|
|
|
|
|
:Returns:
name
|
|
|
"""
# Check for multiple Z coordinates
# Adjust dim if necessary
if dim == 'Z':
z_count = 0
z_names =[]
for mycoord in list(field.coords()):
if field.coord(mycoord).Z:
z_count += 1
z_names.append(mycoord)
if z_count > 1:
dim = z_names[-1]
id = getattr(field.construct(dim), 'id', False)
ncvar = field.construct(dim).nc_get_variable(False)
short_name = getattr(field.construct(dim), 'short_name', False)
long_name = getattr(field.construct(dim), 'long_name', False)
standard_name = getattr(field.construct(dim), 'standard_name', False)
name = 'No Name'
if id:
name = id
if ncvar:
name = ncvar
if short_name:
name = short_name
if long_name:
name = long_name
if standard_name:
name = standard_name
return name
def cf_var_name_titles(field=None, dim=None):
"""
| cf_var_name - return the name from a supplied dimension
| in the following preference order:
| standard_name
| long_name
| short_name
| ncvar
|
| field=None - field
| dim=None - dimension required - 'dim0', 'dim1' etc.
|
:Returns:
name
"""
name = None
units = None
if field.has_construct(dim):
id = getattr(field.construct(dim), 'id', False)
ncvar = field.construct(dim).nc_get_variable(False)
short_name = getattr(field.construct(dim), 'short_name', False)
long_name = getattr(field.construct(dim), 'long_name', False)
standard_name = getattr(field.construct(dim), 'standard_name', False)
#name = 'No Name'
if id:
name = id
if ncvar:
name = ncvar
if short_name:
name = short_name
if long_name:
name = long_name
if standard_name:
name = standard_name
units = getattr(field.construct(dim), 'units', '')
if len(units) > 0:
units = '(' + units + ')'
return name, units
[docs]
def process_color_scales():
"""
| Process colour scales to generate images of them for the web
| documentation and the rst code for inclusion in the
| colour_scale.rst file.
|
|
| No inputs
| This is an internal routine and not used by the user
|
|
|
|
|
:Returns:
None
|
|
|
"""
# Define scale categories
uniform = ['viridis', 'magma', 'inferno', 'plasma', 'parula', 'gray']
ncl_large = ['amwg256', 'BkBlAqGrYeOrReViWh200', 'BlAqGrYeOrRe',
'BlAqGrYeOrReVi200', 'BlGrYeOrReVi200', 'BlRe', 'BlueRed',
'BlueRedGray', 'BlueWhiteOrangeRed', 'BlueYellowRed',
'BlWhRe', 'cmp_b2r', 'cmp_haxby', 'detail', 'extrema',
'GrayWhiteGray', 'GreenYellow', 'helix', 'helix1',
'hotres', 'matlab_hot', 'matlab_hsv', 'matlab_jet',
'matlab_lines', 'ncl_default', 'ncview_default',
'OceanLakeLandSnow', 'rainbow', 'rainbow_white_gray',
'rainbow_white', 'rainbow_gray', 'tbr_240_300',
'tbr_stdev_0_30', 'tbr_var_0_500', 'tbrAvg1', 'tbrStd1',
'tbrVar1', 'thelix', 'ViBlGrWhYeOrRe', 'wh_bl_gr_ye_re',
'WhBlGrYeRe', 'WhBlReWh', 'WhiteBlue',
'WhiteBlueGreenYellowRed', 'WhiteGreen',
'WhiteYellowOrangeRed', 'WhViBlGrYeOrRe', 'WhViBlGrYeOrReWh',
'wxpEnIR', '3gauss', '3saw', 'BrBG']
ncl_meteoswiss = ['hotcold_18lev', 'hotcolr_19lev', 'mch_default',
'perc2_9lev', 'percent_11lev', 'precip2_15lev',
'precip2_17lev', 'precip3_16lev', 'precip4_11lev',
'precip4_diff_19lev', 'precip_11lev',
'precip_diff_12lev', 'precip_diff_1lev',
'rh_19lev', 'spread_15lev']
ncl_color_blindness = ['StepSeq25', 'posneg_2', 'posneg_1',
'BlueDarkOrange18', 'BlueDarkRed18',
'GreenMagenta16', 'BlueGreen14', 'BrownBlue12',
'Cat12']
ncl_small = ['amwg', 'amwg_blueyellowred', 'BlueDarkRed18',
'BlueDarkOrange18', 'BlueGreen14', 'BrownBlue12', 'Cat12',
'cmp_flux', 'cosam12', 'cosam', 'GHRSST_anomaly',
'GreenMagenta16', 'hotcold_18lev', 'hotcolr_19lev',
'mch_default', 'nrl_sirkes', 'nrl_sirkes_nowhite',
'perc2_9lev', 'percent_11lev', 'posneg_2', 'prcp_1', 'prcp_2',
'prcp_3', 'precip_11lev', 'precip_diff_12lev',
'precip_diff_1lev', 'precip2_15lev', 'precip2_17lev',
'precip3_16lev', 'precip4_11lev', 'precip4_diff_19lev',
'radar', 'radar_1', 'rh_19lev', 'seaice_1', 'seaice_2',
'so4_21', 'spread_15lev', 'StepSeq25', 'sunshine_9lev',
'sunshine_diff_12lev', 'temp_19lev', 'temp_diff_18lev',
'temp_diff_1lev', 'topo_15lev', 'wgne15', 'wind_17lev']
orography = ['os250kmetres', 'wiki_1_0_2', 'wiki_1_0_3',
'wiki_2_0', 'wiki_2_0_reduced', 'arctic']
idl_guide = []
for i in np.arange(1, 45):
idl_guide.append('scale' + str(i))
for category in ['uniform', 'ncl_meteoswiss', 'ncl_small', 'ncl_large',
'ncl_color_blindness', 'orography', 'idl_guide']:
if category == 'uniform':
scales = uniform
div = '================== ====='
chars = 10
title = 'Perceptually uniform colour maps for use with continuous '
title += 'data'
print(title)
print('----------------------------------------------')
print('')
print(div)
print('Name Scale')
print(div)
if category == 'ncl_meteoswiss':
scales = ncl_meteoswiss
div = '================== ====='
chars = 19
print('NCAR Command Language - MeteoSwiss colour maps')
print('----------------------------------------------')
print('')
print(div)
print('Name Scale')
print(div)
if category == 'ncl_small':
scales = ncl_small
div = '=================== ====='
chars = 20
print('NCAR Command Language - small color maps (<50 colours)')
print('------------------------------------------------------')
print('')
print(div)
print('Name Scale')
print(div)
if category == 'ncl_large':
scales = ncl_large
div = '======================= ====='
chars = 24
print('NCAR Command Language - large colour maps (>50 colours)')
print('-------------------------------------------------------')
print('')
print(div)
print('Name Scale')
print(div)
if category == 'ncl_color_blindness':
scales = ncl_color_blindness
div = '================ ====='
chars = 17
title = 'NCAR Command Language - Enhanced to help with colour'
title += 'blindness'
print(title)
title = '-----------------------------------------------------'
title += '---------'
print(title)
print('')
print(div)
print('Name Scale')
print(div)
chars = 17
if category == 'orography':
scales = orography
div = '================ ====='
chars = 17
print('Orography/bathymetry colour scales')
print('----------------------------------')
print('')
print(div)
print('Name Scale')
print(div)
chars = 17
if category == 'idl_guide':
scales = idl_guide
div = '======= ====='
chars = 8
print('IDL guide scales')
print('----------------')
print('')
print(div)
print('Name Scale')
print(div)
chars = 8
for scale in scales:
# Make image of scale
fig = plot.figure(figsize=(8, 0.5))
ax1 = fig.add_axes([0.05, 0.1, 0.9, 0.2])
cscale(scale)
cmap = matplotlib.colors.ListedColormap(plotvars.cs)
cb1 = matplotlib.colorbar.ColorbarBase(
ax1, cmap=cmap, orientation='horizontal', ticks=None)
cb1.set_ticks([0.0, 1.0])
cb1.set_ticklabels(['', ''])
file = '/home/andy/cf-docs/cfplot_sphinx/images/'
file += 'colour_scales/' + scale + '.png'
plot.savefig(file)
plot.close()
# Use convert to trim the png file to remove white space
subprocess.call(["convert", "-trim", file, file])
name_pad = scale
while len(name_pad) < chars:
name_pad = name_pad + ' '
fn = name_pad + '.. image:: images/colour_scales/' + scale + '.png'
print(fn)
print(div)
print('')
print('')
[docs]
def reset():
"""
| reset all plotting variables
|
|
|
|
|
|
|
:Returns:
name
|
|
|
"""
axes()
cscale()
levs()
gset()
mapset()
setvars()
[docs]
def setvars(file=None, title_fontsize=None, text_fontsize=None,
colorbar_fontsize=None, colorbar_fontweight=None,
axis_label_fontsize=None, title_fontweight=None,
text_fontweight=None, axis_label_fontweight=None, fontweight=None,
continent_thickness=None, continent_color=None,
continent_linestyle=None, viewer=None,
tspace_year=None, tspace_month=None, tspace_day=None,
tspace_hour=None, xtick_label_rotation=None,
xtick_label_align=None, ytick_label_rotation=None,
ytick_label_align=None, legend_text_weight=None,
legend_text_size=None, cs_uniform=None,
master_title=None, master_title_location=None,
master_title_fontsize=None, master_title_fontweight=None,
dpi=None, land_color=None, ocean_color=None,
lake_color=None, feature_zorder=None,
rotated_grid_spacing=None, rotated_deg_spacing=None,
rotated_continents=None, rotated_grid=None,
rotated_labels=None, rotated_grid_thickness=None,
legend_frame=None,
legend_frame_edge_color=None, legend_frame_face_color=None,
degsym=None, axis_width=None, grid=None,
grid_x_spacing=None, grid_y_spacing=None, grid_zorder=None,
grid_colour=None, grid_linestyle=None, grid_thickness=None,
tight=None, level_spacing=None):
"""
| setvars - set plotting variables and their defaults
|
| file=None - output file name
| title_fontsize=None - title fontsize, default=15
| title_fontweight='normal' - title fontweight
| text_fontsize='normal' - text font size, default=11
| text_fontweight='normal' - text font weight
| axis_label_fontsize=None - axis label fontsize, default=11
| axis_label_fontweight='normal' - axis font weight
| legend_text_size='11' - legend text size
| legend_text_weight='normal' - legend text weight
| colorbar_fontsize='11' - colorbar text size
| colorbar_fontweight='normal' - colorbar font weight
| legend_text_weight='normal' - legend text weight
| master_title_fontsize=30 - master title font size
| master_title_fontweight='normal' - master title font weight
| continent_thickness=1.5 - default=1.5
| continent_color='k' - default='k' (black)
| continent_linestyle='solid' - default='k' (black)
| viewer='display' - use ImageMagick display program
| 'matplotlib' to use image widget to view the picture
| tspace_year=None - time axis spacing in years
| tspace_month=None - time axis spacing in months
| tspace_day=None - time axis spacing in days
| tspace_hour=None - time axis spacing in hours
| xtick_label_rotation=0 - rotation of xtick labels
| xtick_label_align='center' - alignment of xtick labels
| ytick_label_rotation=0 - rotation of ytick labels
| ytick_label_align='right' - alignment of ytick labels
| cs_uniform=True - make a uniform differential colour scale
| master_title=None - master title text
| master_title_location=[0.5,0.95] - master title location
| dpi=None - dots per inch setting
| land_color=None - land colour
| ocean_color=None - ocean colour
| lake_color=None - lake colour
| feature_zorder=None - plotting zorder for above three features
| rotated_grid_spacing=10 - rotated grid spacing in degrees
| rotated_deg_spacing=0.75 - rotated grid spacing between graticule dots
| rotated_deg_tkickness=1.0 - rotated grid thickness for longitude and latitude lines
| rotated_continents=True - draw rotated continents
| rotated_grid=True - draw rotated grid
| rotated_labels=True - draw rotated grid labels
| legend_frame=True - draw a frame around a lineplot legend
| legend_frame_edge_color='k' - color for the legend frame
| legend_frame_face_color=None - color for the legend background
| degsym=True - add degree symbol to longitude and latitude axis labels
| axis_width=None - width of line for the axes
| grid=True - draw grid
| grid_x_spacing=60 - grid longitude spacing in degrees
| grid_x_spacing=30 - grid latitude spacing in degrees
| grid_colour='k' - grid colour
| grid_linestyle='--' - grid line style
| grid_zorder=100 - plotting order for the grid lines
| grid_thickness=1.0 - grid thickness
| tight=False - remove whitespace around the plot
| level_spacing=None - default contour level spacing - takes 'linear', 'log', 'loglike',
| 'outlier' and 'inspect'
|
| Use setvars() to reset to the defaults
|
|
|
:Returns:
name
|
|
|
"""
vals = [file, title_fontsize, text_fontsize, axis_label_fontsize,
continent_thickness, title_fontweight, text_fontweight,
axis_label_fontweight, fontweight, continent_color,
continent_linestyle, tspace_year,
tspace_month, tspace_day, tspace_hour, xtick_label_rotation,
xtick_label_align, ytick_label_rotation, ytick_label_align,
legend_text_size, legend_text_weight, cs_uniform,
master_title, master_title_location,
master_title_fontsize, master_title_fontweight, dpi,
land_color, ocean_color, lake_color, feature_zorder, rotated_grid_spacing,
rotated_deg_spacing, rotated_continents, rotated_grid,
rotated_grid_thickness,
rotated_labels, colorbar_fontsize, colorbar_fontweight,
legend_frame, legend_frame_edge_color, legend_frame_face_color,
degsym, axis_width, grid, grid_x_spacing, grid_y_spacing, grid_zorder,
grid_colour, grid_linestyle, grid_thickness, tight, level_spacing]
if all(val is None for val in vals):
plotvars.file = None
plotvars.title_fontsize = 15
plotvars.text_fontsize = 11
plotvars.colorbar_fontsize = 11
plotvars.axis_label_fontsize = 11
plotvars.title_fontweight = 'normal'
plotvars.text_fontweight = 'normal'
plotvars.colorbar_fontweight = 'normal'
plotvars.axis_label_fontweight = 'normal'
plotvars.fontweight = 'normal'
plotvars.continent_thickness = None
plotvars.continent_color = None
plotvars.continent_linestyle = None
plotvars.tspace_year = None
plotvars.tspace_month = None
plotvars.tspace_day = None
plotvars.tspace_hour = None
plotvars.xtick_label_rotation = 0
plotvars.xtick_label_align = 'center'
plotvars.ytick_label_rotation = 0
plotvars.ytick_label_align = 'right'
plotvars.legend_text_size = 11
plotvars.legend_text_weight = 'normal'
plotvars.cs_uniform = True
plotvars.viewer = plotvars.global_viewer
plotvars.master_title = None
plotvars.master_title_location = [0.5, 0.95]
plotvars.master_title_fontsize = 30
plotvars.master_title_fontweight = 'normal'
plotvars.dpi = None
plotvars.land_color = None
plotvars.ocean_color = None
plotvars.lake_color = None
plotvars.feature_zorder = 100
plotvars.rotated_grid_spacing = 10
plotvars.rotated_deg_spacing = 0.75
plotvars.rotated_grid_thickness = 1.0
plotvars.rotated_continents = True
plotvars.rotated_grid = True
plotvars.rotated_labels = True
plotvars.legend_frame = True
plotvars.legend_frame_edge_color = 'k'
plotvars.legend_frame_face_color = None
plotvars.degsym = False
plotvars.axis_width = None
plotvars.grid = True
plotvars.grid_x_spacing = 60
plotvars.grid_y_spacing = 30
plotvars.grid_colour = 'k'
plotvars.grid_linestyle = '--'
plotvars.grid_thickness = 1.0
plotvars.grid_zorder = 100
matplotlib.pyplot.ioff()
plotvars.tight = False
plotvars.level_spacing = None
if file is not None:
plotvars.file = file
if title_fontsize is not None:
plotvars.title_fontsize = title_fontsize
if axis_label_fontsize is not None:
plotvars.axis_label_fontsize = axis_label_fontsize
if continent_thickness is not None:
plotvars.continent_thickness = continent_thickness
if continent_color is not None:
plotvars.continent_color = continent_color
if continent_linestyle is not None:
plotvars.continent_linestyle = continent_linestyle
if text_fontsize is not None:
plotvars.text_fontsize = colorbar_fontsize
if colorbar_fontsize is not None:
plotvars.colorbar_fontsize = colorbar_fontsize
if text_fontweight is not None:
plotvars.text_fontweight = text_fontweight
if axis_label_fontweight is not None:
plotvars.axis_label_fontweight = axis_label_fontweight
if colorbar_fontweight is not None:
plotvars.colorbar_fontweight = colorbar_fontweight
if title_fontweight is not None:
plotvars.title_fontweight = title_fontweight
if viewer is not None:
plotvars.viewer = viewer
if tspace_year is not None:
plotvars.tspace_year = tspace_year
if tspace_month is not None:
plotvars.tspace_month = tspace_month
if tspace_day is not None:
plotvars.tspace_day = tspace_day
if tspace_hour is not None:
plotvars.tspace_hour = tspace_hour
if xtick_label_rotation is not None:
plotvars.xtick_label_rotation = xtick_label_rotation
if xtick_label_align is not None:
plotvars.xtick_label_align = xtick_label_align
if ytick_label_rotation is not None:
plotvars.ytick_label_rotation = ytick_label_rotation
if ytick_label_align is not None:
plotvars.ytick_label_align = ytick_label_align
if legend_text_size is not None:
plotvars.legend_text_size = legend_text_size
if legend_text_weight is not None:
plotvars.legend_text_weight = legend_text_weight
if cs_uniform is not None:
plotvars.cs_uniform = cs_uniform
if master_title is not None:
plotvars.master_title = master_title
if master_title_location is not None:
plotvars.master_title_location = master_title_location
if master_title_fontsize is not None:
plotvars.master_title_fontsize = master_title_fontsize
if master_title_fontweight is not None:
plotvars.master_title_fontweight = master_title_fontweight
if dpi is not None:
plotvars.dpi = dpi
if land_color is not None:
plotvars.land_color = land_color
if ocean_color is not None:
plotvars.ocean_color = ocean_color
if lake_color is not None:
plotvars.lake_color = lake_color
if feature_zorder is not None:
plotvars.feature_zorder = 999
if rotated_grid_spacing is not None:
plotvars.rotated_grid_spacing = rotated_grid_spacing
if rotated_deg_spacing is not None:
plotvars.rotated_deg_spacing = rotated_deg_spacing
if rotated_grid_thickness is not None:
plotvars.rotated_grid_thickness = rotated_grid_thickness
if rotated_continents is not None:
plotvars.rotated_continents = rotated_continents
if rotated_grid is not None:
plotvars.rotated_grid = rotated_grid
if rotated_labels is not None:
plotvars.rotated_labels = rotated_labels
if legend_frame is not None:
plotvars.legend_frame = legend_frame
if legend_frame_edge_color is not None:
plotvars.legend_frame_edge_color = legend_frame_edge_color
if legend_frame_face_color is not None:
plotvars.legend_frame_face_color = legend_frame_face_color
if degsym is not None:
plotvars.degsym = degsym
if axis_width is not None:
plotvars.axis_width = axis_width
if grid is not None:
plotvars.grid = grid
if grid_x_spacing is not None:
plotvars.grid_x_spacing = grid_x_spacing
if grid_y_spacing is not None:
plotvars.grid_y_spacing = grid_y_spacing
if grid_colour is not None:
plotvars.grid_colour = grid_colour
if grid_linestyle is not None:
plotvars.grid_linestyle = grid_linestyle
if grid_thickness is not None:
plotvars.grid_thickness = grid_thickness
if grid_zorder is not None:
plotvars.grid_zorder = grid_zorder
if tight is not None:
plotvars.tight = tight
if level_spacing is not None:
plotvars.level_spacing = level_spacing
[docs]
def vloc(xvec=None, yvec=None, lons=None, lats=None):
"""
| vloc is used to locate the positions of a set of points in a vector
|
|
|
| xvec=None - data longitudes
| yvec=None - data latitudes
| lons=None - required longitude positions
| lats=None - required latitude positions
:Returns:
locations of user points in the longitude and latitude points
|
|
|
|
|
|
|
"""
# Check input parameters
if any(val is None for val in [xvec, yvec, lons, lats]):
errstr = '\nvloc error\n'
errstr += 'xvec, yvec, lons, lats all need to be passed to vloc to\n'
errstr += 'generate a set of location points\n'
raise Warning(errstr)
xarr = np.zeros(np.size(lons))
yarr = np.zeros(np.size(lats))
# Convert longitudes to -180 to 180.
for i in np.arange(np.size(xvec)):
xvec[i] = ((xvec[i] + 180) % 360) - 180
for i in np.arange(np.size(lons)):
lons[i] = ((lons[i] + 180) % 360) - 180
# Centre around 180 degrees longitude if needed.
if (max(xvec) > 150):
for i in np.arange(np.size(xvec)):
xvec[i] = (xvec[i] + 360.0) % 360.0
pts = np.where(xvec < 0.0)
xvec[pts] = xvec[pts] + 360.0
for i in np.arange(np.size(lons)):
lons[i] = (lons[i] + 360.0) % 360.0
pts = np.where(lons < 0.0)
lons[pts] = lons[pts] + 360.0
# Find position in array
for i in np.arange(np.size(lons)):
if ((lons[i] < min(xvec)) or (lons[i] > max(xvec))):
xpt = -1
else:
xpts = np.where(lons[i] >= xvec)
xpt = np.nanmax(xpts)
if ((lats[i] < min(yvec)) or (lats[i] > max(yvec))):
ypt = -1
else:
ypts = np.where(lats[i] >= yvec)
ypt = np.nanmax(ypts)
if (xpt >= 0):
xarr[i] = xpt + (lons[i] - xvec[xpt]) / (xvec[xpt + 1] - xvec[xpt])
else:
xarr[i] = None
if (ypt >= 0) and ypt <= np.size(yvec) - 2:
yarr[i] = ypt + (lats[i] - yvec[ypt]) / (yvec[ypt + 1] - yvec[ypt])
else:
yarr[i] = None
return (xarr, yarr)
[docs]
def rgaxes(xpole=None, ypole=None, xvec=None, yvec=None,
xticks=None, xticklabels=None, yticks=None, yticklabels=None,
axes=None, xaxis=None, yaxis=None, xlabel=None, ylabel=None):
"""
| rgaxes - label rotated grid plots
|
| xpole=None - location of xpole in degrees
| ypole=None - location of ypole in degrees
| xvec=None - location of x grid points
| yvec=None - location of y grid points
|
| axes=True - plot x and y axes
| xaxis=True - plot xaxis
| yaxis=True - plot y axis
| xticks=None - xtick positions
| xticklabels=None - xtick labels
| yticks=None - y tick positions
| yticklabels=None - ytick labels
| xlabel=None - label for x axis
| ylabel=None - label for y axis
|
:Returns:
name
|
|
|
|
|
|
"""
spacing = plotvars.rotated_grid_spacing
degspacing = plotvars.rotated_deg_spacing
continents = plotvars.rotated_continents
grid = plotvars.rotated_grid
labels = plotvars.rotated_labels
grid_thickness = plotvars.rotated_grid_thickness
# Invert y array if going from north to south
# Otherwise this gives nans for all output
yvec_orig = yvec
if (yvec[0] > yvec[np.size(yvec) - 1]):
yvec = yvec[::-1]
gset(xmin=0, xmax=np.size(xvec) - 1,
ymin=0, ymax=np.size(yvec) - 1, user_gset=0)
# Set continent thickness and color if not already set
if plotvars.continent_thickness is None:
continent_thickness = 1.5
if plotvars.continent_color is None:
continent_color = 'k'
# Draw continents
if continents:
import cartopy.io.shapereader as shpreader
import shapefile
shpfilename = shpreader.natural_earth(resolution=plotvars.resolution,
category='physical',
name='coastline')
reader = shapefile.Reader(shpfilename)
shapes = [s.points for s in reader.shapes()]
for shape in shapes:
lons, lats = list(zip(*shape))
lons = np.array(lons)
lats = np.array(lats)
rotated_transform = ccrs.RotatedPole(pole_latitude=ypole, pole_longitude=xpole)
points = rotated_transform.transform_points(ccrs.PlateCarree(), lons, lats)
xout = np.array(points)[:, 0]
yout = np.array(points)[:, 1]
xpts, ypts = vloc(lons=xout, lats=yout, xvec=xvec, yvec=yvec)
plotvars.plot.plot(xpts, ypts, linewidth=continent_thickness,
color=continent_color)
if xticks is None:
lons = -180 + np.arange(360 / spacing + 1) * spacing
else:
lons = xticks
if yticks is None:
lats = -90 + np.arange(180 / spacing + 1) * spacing
else:
lats = yticks
# Work out how far from plot to plot the longitude and latitude labels
xlim = plotvars.plot.get_xlim()
spacing_x = (xlim[1] - xlim[0]) / 20
ylim = plotvars.plot.get_ylim()
spacing_y = (ylim[1] - ylim[0]) / 20
spacing = min(spacing_x, spacing_y)
# Draw lines along a longitude
if axes:
if xaxis:
for val in np.arange(np.size(lons)):
ipts = 179.0 / degspacing
lona = np.zeros(int(ipts)) + lons[val]
lata = -90 + np.arange(ipts - 1) * degspacing
rotated_transform = ccrs.RotatedPole(pole_latitude=ypole, pole_longitude=xpole)
points = rotated_transform.transform_points(ccrs.PlateCarree(), lona, lata)
xout = np.array(points)[:, 0]
yout = np.array(points)[:, 1]
xpts, ypts = vloc(lons=xout, lats=yout, xvec=xvec, yvec=yvec)
if grid:
plotvars.plot.plot(xpts, ypts, ':', linewidth=grid_thickness,
color='k')
if labels:
# Make a label unless the axis is all Nans
if (np.size(ypts[5:]) > np.sum(np.isnan(ypts[5:]))):
ymin = np.nanmin(ypts[5:])
loc = np.where(ypts == ymin)[0]
if np.size(loc) > 1:
loc = loc[1]
if loc > 0:
if np.isfinite(xpts[loc]):
line = matplotlib.lines.Line2D(
[xpts[loc], xpts[loc]], [0, -spacing/2], color='k')
plotvars.plot.add_line(line)
line.set_clip_on(False)
fw = plotvars.text_fontweight
if xticklabels is None:
xticklabel = mapaxis(lons[val], lons[val], type=1)[1][0]
else:
xticklabel = xticks[val]
plotvars.plot.text(xpts[loc], -spacing,
xticklabel,
horizontalalignment='center',
verticalalignment='top',
fontsize=plotvars.text_fontsize,
fontweight=fw)
# Draw lines along a latitude
if axes:
if yaxis:
for val in np.arange(np.size(lats)):
ipts = 359.0 / degspacing
lata = np.zeros(int(ipts)) + lats[val]
lona = -180.0 + np.arange(ipts - 1) * degspacing
rotated_transform = ccrs.RotatedPole(pole_latitude=ypole, pole_longitude=xpole)
points = rotated_transform.transform_points(ccrs.PlateCarree(), lona, lata)
xout = np.array(points)[:, 0]
yout = np.array(points)[:, 1]
xpts, ypts = vloc(lons=xout, lats=yout, xvec=xvec, yvec=yvec)
if grid:
plotvars.plot.plot(xpts, ypts, ':', linewidth=grid_thickness,
color='k')
if labels:
# Make a label unless the axis is all Nans
if (np.size(xpts[5:]) > np.sum(np.isnan(xpts[5:]))):
xmin = np.nanmin(xpts[5:])
loc = np.where(xpts == xmin)[0]
if np.size(loc) == 1:
if loc > 0:
if np.isfinite(ypts[loc]):
line = matplotlib.lines.Line2D(
[0, -spacing/2], [ypts[loc], ypts[loc]], color='k')
plotvars.plot.add_line(line)
line.set_clip_on(False)
fw = plotvars.text_fontweight
if yticklabels is None:
yticklabel = mapaxis(lats[val], lats[val], type=2)[1][0]
else:
yticklabel = yticks[val]
plotvars.plot.text(-spacing, ypts[loc],
yticklabel,
horizontalalignment='right',
verticalalignment='center',
fontsize=plotvars.text_fontsize,
fontweight=fw)
# Reset yvec
yvec = yvec_orig
[docs]
def lineplot(f=None, x=None, y=None, fill=True, lines=True, line_labels=True,
title=None, ptype=0, linestyle='-', linewidth=1.0, color=None,
xlog=False, ylog=False, verbose=None, swap_xy=False,
marker=None, markersize=5.0, markeredgecolor='k',
markeredgewidth=0.5, label=None,
legend_location='upper right', xunits=None, yunits=None,
xlabel=None, ylabel=None, xticks=None, yticks=None,
xticklabels=None, yticklabels=None, xname=None, yname=None,
axes=True, xaxis=True, yaxis=True, titles=False, zorder=None):
"""
| lineplot is the interface to line plotting in cf-plot.
| The minimum use is lineplot(f) where f is a CF field.
| If x and y are passed then an appropriate plot is made allowing
| x vs data and y vs data plots.
| When making a labelled line plot:
| always have a label for each line
| always put the legend location as an option to the last call to lineplot
|
| f - CF data used to make a line plot
| x - x locations of data in y
| y - y locations of data in x
| linestyle='-' - line style
| color=None - line color. Defaults to Matplotlib colour scheme unless specified
| linewidth=1.0 - line width
| marker=None - marker for points along the line
| markersize=5.0 - size of the marker
| markeredgecolor = 'k' - colour of edge around the marker
| markeredgewidth = 0.5 - width of edge around the marker
| xlog=False - log x-axis
| ylog=False - log y-axis
| label=None - line label - label for line
| legend_location='upper right' - default location of legend
| Other options are {'best': 0, 'center': 10, 'center left': 6,
| 'center right': 7, 'lower center': 8,
| 'lower left': 3, 'lower right': 4, 'right': 5,
| 'upper center': 9, 'upper left': 2, 'upper right': 1}
| titles=False - set to True to have a dimensions title
| verbose=None - change to 1 to get a verbose listing of what lineplot
| is doing
| zorder=None - plotting order
|
| The following parameters override any CF data defaults:
| title=None - plot title
| xunits=None - x units
| yunits=None - y units
| xlabel=None - x name
| ylabel=None - y name
| xname=None - depreciated keyword
| yname=None - depreciated keyword
| xticks=None - x ticks
| xticklabels=None - x tick labels
| yticks=None - y ticks
| yticklabels - y tick labels
| axes=True - plot x and y axes
| xaxis=True - plot xaxis
| yaxis=True - plot y axis
|
|
| When making a multiple line plot:
| a) set the axis limits with gset before plotting the lines
| b) the last call to lineplot is the one that any of the above
| axis overrides should be placed in.
|
|
"""
if verbose:
print('lineplot - making a line plot')
# Catch depreciated keywords
if xname is not None or yname is not None:
print('\nlineplot error')
print('xname and yname are now depreciated keywords')
print('Please use xlabel and ylabel\n')
return
##################
# Open a new plot is necessary
##################
if plotvars.user_plot == 0:
gopen(user_plot=0)
# Call gpos(1) if not already called
if plotvars.rows > 1 or plotvars.columns > 1:
if plotvars.gpos_called is False:
gpos(1)
##################
# Extract required data
# If a cf-python field
##################
cf_field = False
if f is not None:
if isinstance(f, cf.Field):
cf_field = True
# Check data is 1D
ndims = np.squeeze(f.data).ndim
if ndims != 1:
errstr = "\n\ncfp.lineplot error need a 1 dimensonal field to make a line\n"
errstr += "received " + str(np.squeeze(f.data).ndim) + " dimensions\n\n"
raise TypeError(errstr)
if x is not None:
if isinstance(x, cf.Field):
errstr = "\n\ncfp.lineplot error - two or more cf-fields passed for plotting.\n"
errstr += "To plot two cf-fields open a graphics plot with cfp.gopen(), \n"
errstr += "plot the two fields separately with cfp.lineplot and then close\n"
errstr += "the graphics plot with cfp.gclose()\n\n"
raise TypeError(errstr)
elif isinstance(f, cf.FieldList):
errstr = "\n\ncfp.lineplot - cannot plot a field list\n\n"
raise TypeError(errstr)
plot_xlabel = ''
plot_ylabel = ''
xlabel_units = ''
ylabel_units = ''
if cf_field:
# Extract data
if verbose:
print('lineplot - CF field, extracting data')
has_count = 0
for mydim in list(f.dimension_coordinates()):
if np.size(np.squeeze(f.construct(mydim).array)) > 1:
has_count = has_count + 1
x = np.squeeze(f.construct(mydim).array)
# x label
xlabel_units = str(getattr(f.construct(mydim), 'Units', ''))
plot_xlabel = cf_var_name(field=f, dim=mydim) + ' ('
plot_xlabel += xlabel_units + ')'
y = np.squeeze(f.array)
# y label
if hasattr(f, 'id'):
plot_ylabel = f.id
nc = f.nc_get_variable(False)
if nc:
plot_ylabel = f.nc_get_variable()
if hasattr(f, 'short_name'):
plot_ylabel = f.short_name
if hasattr(f, 'long_name'):
plot_ylabel = f.long_name
if hasattr(f, 'standard_name'):
plot_ylabel = f.standard_name
if hasattr(f, 'Units'):
ylabel_units = str(f.Units)
else:
ylabel_units = ''
plot_ylabel += ' (' + ylabel_units + ')'
if has_count != 1:
errstr = '\n lineplot error - passed field is not suitable '
errstr += 'for plotting as a line\n'
for mydim in list(f.dimension_coordinates()):
sn = getattr(f.construct(mydim), 'standard_name', False)
ln = getattr(f.construct(mydim), 'long_name', False)
if sn:
errstr = errstr + \
str(mydim) + ',' + str(sn) + ',' + \
str(f.construct(mydim).size) + '\n'
else:
if ln:
errstr = errstr + \
str(mydim) + ',' + str(ln) + ',' + \
str(f.construct(mydim).size) + '\n'
raise Warning(errstr)
else:
if verbose:
print('lineplot - not a CF field, using passed data')
errstr = ''
if x is None or y is None:
errstr = 'lineplot error- must define both x and y'
if f is not None:
errstr += 'lineplot error- must define just x and y to make '
errstr += 'a lineplot'
if errstr != '':
raise Warning('\n' + errstr + '\n')
# Z on y-axis
ztype = None
if xlabel_units in ['mb', 'mbar', 'millibar', 'decibar',
'atmosphere', 'atm', 'pascal', 'Pa', 'hPa']:
ztype = 1
if xlabel_units in ['meter', 'metre', 'm', 'kilometer', 'kilometre', 'km']:
ztype = 2
myz = find_z(f)
if cf_field and f.has_construct(myz):
z_coord = f.construct(myz)
if len(z_coord.array) > 1:
zlabel = ''
if hasattr(z_coord, 'long_name'):
zlabel = z_coord.long_name
if hasattr(z_coord, 'standard_name'):
zlabel = z_coord.standard_name
if zlabel == 'atmosphere_hybrid_height_coordinate':
ztype = 2
if ztype is not None:
x, y = y, x
plot_xlabel, plot_ylabel = plot_ylabel, plot_xlabel
# Set data values
if verbose:
print('lineplot - setting data values')
xpts = np.squeeze(x)
ypts = np.squeeze(y)
minx = np.min(x)
miny = np.min(y)
maxx = np.max(x)
maxy = np.max(y)
# Use accumulated plot limits if making a multiple line plot
if plotvars.graph_xmin is None:
plotvars.graph_xmin = minx
else:
if minx < plotvars.graph_xmin:
plotvars.graph_xmin = minx
if plotvars.graph_xmax is None:
plotvars.graph_xmax = maxx
else:
if maxx > plotvars.graph_xmax:
plotvars.graph_xmax = maxx
if plotvars.graph_ymin is None:
plotvars.graph_ymin = miny
else:
if miny < plotvars.graph_ymin:
plotvars.graph_ymin = miny
if plotvars.graph_ymax is None:
plotvars.graph_ymax = maxy
else:
if maxy > plotvars.graph_ymax:
plotvars.graph_ymax = maxy
# Reset plot limits based on accumulated plot limits
minx = plotvars.graph_xmin
maxx = plotvars.graph_xmax
miny = plotvars.graph_ymin
maxy = plotvars.graph_ymax
if cf_field and f.has_construct('T'):
taxis = f.construct('T')
if ztype == 1:
miny = np.max(y)
maxy = np.min(y)
if ztype == 2:
if cf_field and f.has_construct('Z'):
if f.construct('Z').positive == 'down':
miny = np.max(y)
maxy = np.min(y)
# Use user set values if present
time_xstr = False
time_ystr = False
if plotvars.xmin is not None:
minx = plotvars.xmin
miny = plotvars.ymin
maxx = plotvars.xmax
maxy = plotvars.ymax
# Change from date string to a number if strings are passed
try:
float(minx)
except Exception:
time_xstr = True
try:
float(miny)
except Exception:
time_ystr = True
if cf_field and f.has_construct('T'):
taxis = f.construct('T')
if time_xstr or time_ystr:
ref_time = f.construct('T').units
ref_calendar = f.construct('T').calendar
time_units = cf.Units(ref_time, ref_calendar)
if time_xstr:
t = cf.Data(cf.dt(minx), units=time_units)
minx = t.array
t = cf.Data(cf.dt(maxx), units=time_units)
maxx = t.array
taxis = cf.Data([cf.dt(plotvars.xmin),
cf.dt(plotvars.xmax)], units=time_units)
if time_ystr:
t = cf.Data(cf.dt(miny), units=time_units)
miny = t.array
t = cf.Data(cf.dt(maxy), units=time_units)
maxy = t.array
taxis = cf.Data([cf.dt(plotvars.ymin),
cf.dt(plotvars.ymax)], units=time_units)
# Set x and y labelling
# Retrieve any user defined axis labels
if plot_xlabel == '' and plotvars.xlabel is not None:
plot_xlabel = plotvars.xlabel
if plot_ylabel == '' and plotvars.ylabel is not None:
plot_ylabel = plotvars.ylabel
if xticks is None and plotvars.xticks is not None:
xticks = plotvars.xticks
if plotvars.xticklabels is not None:
xticklabels = plotvars.xticklabels
else:
xticklabels = list(map(str, xticks))
if yticks is None and plotvars.yticks is not None:
yticks = plotvars.yticks
if plotvars.yticklabels is not None:
yticklabels = plotvars.yticklabels
else:
yticklabels = list(map(str, yticks))
mod = False
ymult = 0
if xticks is None:
if plot_xlabel[0:3].lower() == 'lon':
xticks, xticklabels = mapaxis(minx, maxx, type=1)
if plot_xlabel[0:3].lower() == 'lat':
xticks, xticklabels = mapaxis(minx, maxx, type=2)
if cf_field:
if xticks is None:
if f.has_construct('T'):
if np.size(f.construct('T').array) > 1:
xticks, xticklabels, plot_xlabel = timeaxis(taxis)
if xticks is None:
xticks, ymult = gvals(dmin=minx, dmax=maxx, mod=mod)
# Fix long floating point numbers if necessary
fix_floats(xticks)
xticklabels = xticks
else:
if xticklabels is None:
xticklabels = []
for val in xticks:
xticklabels.append('{}'.format(val))
if yticks is None:
if abs(maxy - miny) > 1:
if miny < maxy:
yticks, ymult = gvals(dmin=miny, dmax=maxy, mod=mod)
if maxy < miny:
yticks, ymult = gvals(dmin=maxy, dmax=miny, mod=mod)
else:
yticks, ymult = gvals(dmin=miny, dmax=maxy, mod=mod)
# Fix long floating point numbers if necessary
fix_floats(yticks)
if yticklabels is None:
yticklabels = []
for val in yticks:
yticklabels.append(str(round(val, 9)))
if xlabel is not None:
plot_xlabel = xlabel
if xunits is not None:
plot_xlabel += '('+xunits+')'
if ylabel is not None:
plot_ylabel = ylabel
if yunits is not None:
plot_ylabel += '('+yunits+')'
if swap_xy:
if verbose:
print('lineplot - swapping x and y')
xpts, ypts = ypts, xpts
minx, miny = miny, minx
maxx, maxy = maxy, maxx
plot_xlabel, plot_ylabel = plot_ylabel, plot_xlabel
xticks, yticks = yticks, xticks
xticklabels, yticklabels = yticklabels, xticklabels
if plotvars.user_gset == 1:
if time_xstr is False and time_ystr is False:
minx = plotvars.xmin
maxx = plotvars.xmax
miny = plotvars.ymin
maxy = plotvars.ymax
if axes:
if xaxis is not True:
xticks = [100000000]
xticklabels = xticks
plot_xlabel = ''
if yaxis is not True:
yticks = [100000000]
yticklabels = yticks
plot_ylabel = ''
else:
xticks = [100000000]
xticklabels = xticks
yticks = [100000000]
yticklabels = yticks
plot_xlabel = ''
plot_ylabel = ''
# Generate titles if requested
if titles:
title_dims = generate_titles(f)
# Make graph
if verbose:
print('lineplot - making graph')
xlabelalignment = plotvars.xtick_label_align
ylabelalignment = plotvars.ytick_label_align
if lines is False:
linewidth = 0.0
colorarg = {}
if color is not None:
colorarg = {'color': color}
graph = plotvars.plot
if plotvars.twinx:
graph = graph.twinx()
ylabelalignment = 'left'
if plotvars.twiny:
graph = graph.twiny()
# Reset y limits if minx = maxy
if plotvars.xmin is None:
if miny == maxy:
miny = miny - 1.0
maxy = maxy + 1.0
graph.axis([minx, maxx, miny, maxy])
graph.tick_params(direction='out', which='both', right=True, top=True)
graph.set_xlabel(plot_xlabel, fontsize=plotvars.axis_label_fontsize,
fontweight=plotvars.axis_label_fontweight)
graph.set_ylabel(plot_ylabel, fontsize=plotvars.axis_label_fontsize,
fontweight=plotvars.axis_label_fontweight)
if plotvars.xlog or xlog:
graph.set_xscale('log')
if plotvars.ylog or ylog:
graph.set_yscale('log')
if xticks is not None:
graph.set_xticks(xticks)
graph.set_xticklabels(xticklabels,
rotation=plotvars.xtick_label_rotation,
horizontalalignment=xlabelalignment,
fontsize=plotvars.axis_label_fontsize,
fontweight=plotvars.axis_label_fontweight)
if yticks is not None:
graph.set_yticks(yticks)
graph.set_yticklabels(yticklabels,
rotation=plotvars.ytick_label_rotation,
horizontalalignment=ylabelalignment,
fontsize=plotvars.axis_label_fontsize,
fontweight=plotvars.axis_label_fontweight)
graph.plot(xpts, ypts, **colorarg, linestyle=linestyle,
linewidth=linewidth, marker=marker,
markersize=markersize,
markeredgecolor=markeredgecolor,
markeredgewidth=markeredgewidth,
label=label, zorder=zorder)
# Set axis width if required
if plotvars.axis_width is not None:
for axis in ['top', 'bottom', 'left', 'right']:
plotvars.plot.spines[axis].set_linewidth(plotvars.axis_width)
# Add a legend if needed
if label is not None:
legend_properties = {
'size': plotvars.legend_text_size,
'weight': plotvars.legend_text_weight}
graph.legend(loc=legend_location, prop=legend_properties,
frameon=plotvars.legend_frame,
edgecolor=plotvars.legend_frame_edge_color,
facecolor=plotvars.legend_frame_face_color)
# Set title
if title is not None:
graph.set_title(title, fontsize=plotvars.title_fontsize,
fontweight=plotvars.title_fontweight)
# Titles for dimensions
if titles:
plotvars.plot = graph
plotvars.plot_type = 0
dim_titles(title=title_dims)
##################
# Save or view plot
##################
if plotvars.user_plot == 0:
if verbose:
print('Saving or viewing plot')
gclose()
[docs]
def regression_tests():
"""
| Test for cf-plot regressions
| Run through some standard levs, gvals, lon and lat labelling
| Make all the gallery plots and use Imagemagick to display them
| alongside a reference plot
|
|
|
|
|
"""
print('==================')
print('Regression testing')
print('==================')
print('')
print('------------------')
print('Testing for levels')
print('------------------')
ref_answer = [-35, -30, -25, -20, -15, -10, -5, 0, 5,
10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65]
compare_arrays(ref=ref_answer, levs_test=True, min=-35, max=65, step=5)
ref_answer = [-6., -4.8, -3.6, -2.4, -1.2, 0., 1.2, 2.4, 3.6, 4.8, 6.]
compare_arrays(ref=ref_answer, levs_test=True, min=-6, max=6, step=1.2)
ref_answer = [50000, 51000, 52000, 53000, 54000, 55000, 56000, 57000,
58000, 59000, 60000]
compare_arrays(ref=ref_answer, levs_test=True, min=50000, max=60000, step=1000)
ref_answer = [-7000, -6500, -6000, -5500, -5000, -4500, -4000, -3500,
-3000, -2500, -2000, -1500, -1000, -500]
compare_arrays(
ref=ref_answer,
levs_test=True,
min=-7000,
max=-300,
step=500)
print('')
print('-----------------')
print('Testing for gvals')
print('-----------------')
ref_answer = [281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293]
compare_arrays(ref=ref_answer, min=280.50619506835938,
max=293.48431396484375, mult=0, gvals_test=True)
ref_answer = [0.356, 0.385, 0.414, 0.443, 0.472, 0.501, 0.53, 0.559,
0.588, 0.617, 0.646, 0.675]
compare_arrays(ref=ref_answer, min=0.356, max=0.675, mult=0,
gvals_test=True)
ref_answer = [-45, -40, -35, -30, -25, -20, -15, -10, -5, 0, 5, 10, 15,
20, 25, 30, 35, 40, 45, 50]
compare_arrays(ref=ref_answer, min=-49.510975, max=53.206604, mult=0,
gvals_test=True)
ref_answer = [47000, 48000, 49000, 50000, 51000, 52000, 53000, 54000,
55000, 56000, 57000, 58000, 59000, 60000, 61000, 62000,
63000, 64000]
compare_arrays(ref=ref_answer, min=46956, max=64538, mult=0,
gvals_test=True)
ref_answer = [-1., -0.9, -0.8, -0.7, -0.6, -0.5, -0.4, -0.3, -0.2, -0.1, 0., 0.1]
compare_arrays(ref=ref_answer, min=-1.0, max=0.1, mult=0,
gvals_test=True)
print('')
print('----------------------------------------')
print('Testing for longitude/latitude labelling')
print('----------------------------------------')
ref_answer = ([-180, -120, -60, 0, 60, 120, 180],
['180', '120W', '60W', '0', '60E', '120E', '180'])
compare_arrays(ref=ref_answer, min=-180, max=180, type=1,
mapaxis_test=True)
ref_answer = ([150, 180, 210, 240, 270],
['150E', '180', '150W', '120W', '90W'])
compare_arrays(ref=ref_answer, min=135, max=280, type=1,
mapaxis_test=True)
ref_answer = ([0, 10, 20, 30, 40, 50, 60, 70, 80, 90], ['0', '10E', '20E',
'30E', '40E', '50E', '60E', '70E', '80E', '90E'])
compare_arrays(ref=ref_answer, min=0, max=90, type=1, mapaxis_test=True)
ref_answer = ([-90, -60, -30, 0, 30, 60, 90],
['90S', '60S', '30S', '0', '30N', '60N', '90N'])
compare_arrays(ref=ref_answer, min=-90, max=90, type=2, mapaxis_test=True)
ref_answer = ([0, 5, 10, 15, 20, 25, 30],
['0', '5N', '10N', '15N', '20N', '25N', '30N'])
compare_arrays(ref=ref_answer, min=0, max=30, type=2, mapaxis_test=True)
print('')
print('-----------------')
print('Testing for plots')
print('-----------------')
# Run through gallery examples and compare to reference plots
# example1
reset()
setvars(file='fig1.png')
f = cf.read('/opt/graphics/cfplot_data/tas_A1.nc')[0]
con(f.subspace(time=15))
compare_images(1)
# example2
reset()
setvars(file='fig2.png')
f = cf.read('/opt/graphics/cfplot_data/tas_A1.nc')[0]
con(f.subspace(time=15), blockfill=True, lines=False)
compare_images(2)
# example3
reset()
setvars(file='fig3.png')
f = cf.read('/opt/graphics/cfplot_data/tas_A1.nc')[0]
mapset(lonmin=-15, lonmax=3, latmin=48, latmax=60)
levs(min=265, max=285, step=1)
con(f.subspace(time=15))
compare_images(3)
# example4
reset()
setvars(file='fig4.png')
f = cf.read('/opt/graphics/cfplot_data/ggap.nc')[1]
mapset(proj='npstere')
con(f.subspace(pressure=500))
compare_images(4)
# example5
reset()
setvars(file='fig5.png')
f = cf.read('/opt/graphics/cfplot_data/ggap.nc')[1]
mapset(proj='spstere', boundinglat=-30, lon_0=180)
con(f.subspace(pressure=500))
compare_images(5)
# example6
reset()
setvars(file='fig6.png')
f = cf.read('/opt/graphics/cfplot_data/ggap.nc')[3]
con(f.subspace(longitude=0))
compare_images(6)
# example7
reset()
setvars(file='fig7.png')
f = cf.read('/opt/graphics/cfplot_data/ggap.nc')[1]
con(f.collapse('mean', 'longitude'))
compare_images(7)
# example8
reset()
setvars(file='fig8.png')
f = cf.read('/opt/graphics/cfplot_data/ggap.nc')[1]
con(f.collapse('mean', 'longitude'), ylog=1)
compare_images(8)
# example9
reset()
setvars(file='fig9.png')
f = cf.read('/opt/graphics/cfplot_data/ggap.nc')[0]
con(f.collapse('mean', 'latitude'))
compare_images(9)
# example10
reset()
setvars(file='fig10.png')
f = cf.read('/opt/graphics/cfplot_data/tas_A1.nc')[0]
cscale('plasma')
con(f.subspace(longitude=0), lines=0)
compare_images(10)
# example11
reset()
setvars(file='fig11.png')
f = cf.read('/opt/graphics/cfplot_data/tas_A1.nc')[0]
gset(-30, 30, '1960-1-1', '1980-1-1')
levs(min=280, max=305, step=1)
cscale('plasma')
con(f.subspace(longitude=0), lines=0)
compare_images(11)
# example12
reset()
setvars(file='fig12.png')
f = cf.read('/opt/graphics/cfplot_data/tas_A1.nc')[0]
cscale('plasma')
con(f.subspace(latitude=0), lines=0)
compare_images(12)
# example13
reset()
setvars(file='fig13.png')
f = cf.read('/opt/graphics/cfplot_data/ggap.nc')
u = f[1].subspace(pressure=500)
v = f[3].subspace(pressure=500)
vect(u=u, v=v, key_length=10, scale=100, stride=5)
compare_images(13)
# example14
reset()
setvars(file='fig14.png')
f = cf.read('/opt/graphics/cfplot_data/ggap.nc')
u = f[1].subspace(pressure=500)
v = f[3].subspace(pressure=500)
t = f[0].subspace(pressure=500)
gopen()
mapset(lonmin=10, lonmax=120, latmin=-30, latmax=30)
levs(min=254, max=270, step=1)
con(t)
vect(u=u, v=v, key_length=10, scale=50, stride=2)
gclose()
compare_images(14)
# example15
reset()
setvars(file='fig15.png')
u = cf.read('/opt/graphics/cfplot_data/ggap.nc')[1]
v = cf.read('/opt/graphics/cfplot_data/ggap.nc')[3]
u = u.subspace(Z=500)
v = v.subspace(Z=500)
mapset(proj='npstere')
vect(u=u, v=v, key_length=10, scale=100, pts=40,
title='Polar plot with regular point distribution')
compare_images(15)
# example16
reset()
setvars(file='fig16.png')
c = cf.read('/opt/graphics/cfplot_data/vaAMIPlcd_DJF.nc')[0]
c = c.subspace(Y=cf.wi(-60, 60))
c = c.subspace(X=cf.wi(80, 160))
c = c.collapse('T: mean X: mean')
g = cf.read('/opt/graphics/cfplot_data/wapAMIPlcd_DJF.nc')[0]
g = g.subspace(Y=cf.wi(-60, 60))
g = g.subspace(X=cf.wi(80, 160))
g = g.collapse('T: mean X: mean')
vect(u=c, v=-g, key_length=[5, 0.05],
scale=[20, 0.2], title='DJF', key_location=[0.95, -0.05])
compare_images(16)
# example17
reset()
setvars(file='fig17.png')
f = cf.read('/opt/graphics/cfplot_data/tas_A1.nc')[0]
g = f.subspace(time=15)
gopen()
cscale('magma')
con(g)
stipple(f=g, min=220, max=260, size=100, color='#00ff00')
stipple(f=g, min=300, max=330, size=50, color='#0000ff', marker='s')
gclose()
compare_images(17)
# example18
reset()
setvars(file='fig18.png')
f = cf.read('/opt/graphics/cfplot_data/tas_A1.nc')[0]
g = f.subspace(time=15)
gopen()
cscale('magma')
mapset(proj='npstere')
con(g)
stipple(f=g, min=265, max=295, size=100, color='#00ff00')
gclose()
compare_images(18)
# example19
reset()
setvars(file='fig19.png')
f = cf.read('/opt/graphics/cfplot_data/ggap.nc')[1]
gopen(rows=2, columns=2, bottom=0.2)
gpos(1)
con(f.subspace(pressure=500), colorbar=None)
gpos(2)
mapset(proj='moll')
con(f.subspace(pressure=500), colorbar=None)
gpos(3)
mapset(proj='npstere', boundinglat=30, lon_0=180)
con(f.subspace(pressure=500), colorbar=None)
gpos(4)
mapset(proj='spstere', boundinglat=-30, lon_0=180)
con(f.subspace(pressure=500), colorbar_position=[
0.1, 0.1, 0.8, 0.02], colorbar_orientation='horizontal')
gclose()
compare_images(19)
# example20
reset()
setvars(file='fig20.png')
f = cf.read('/opt/graphics/cfplot_data/Geostropic_Adjustment.nc')[0]
con(f.subspace[9])
compare_images(20)
# example21
reset()
setvars(file='fig21.png')
f = cf.read('/opt/graphics/cfplot_data/Geostropic_Adjustment.nc')[0]
con(f.subspace[9], title='test data',
xticks=np.arange(5) * 100000 + 100000,
yticks=np.arange(7) * 2000 + 2000,
xlabel='x-axis', ylabel='z-axis')
compare_images(21)
# example22
reset()
setvars(file='fig22.png')
f = cf.read_field('/opt/graphics/cfplot_data/rgp.nc')
cscale('gray')
con(f)
compare_images(22)
# example23
reset()
setvars(file='fig23.png')
f = cf.read_field('/opt/graphics/cfplot_data/rgp.nc')
data = f.array
xvec = f.construct('dim1').array
yvec = f.construct('dim0').array
xpole = 160
ypole = 30
gopen()
cscale('plasma')
xpts = np.arange(np.size(xvec))
ypts = np.arange(np.size(yvec))
gset(xmin=0, xmax=np.size(xvec) - 1, ymin=0, ymax=np.size(yvec) - 1)
levs(min=980, max=1035, step=2.5)
con(data, xpts, ypts[::-1])
rgaxes(xpole=xpole, ypole=ypole, xvec=xvec, yvec=yvec)
gclose()
compare_images(23)
# example24
reset()
setvars(file='fig24.png')
from matplotlib.mlab import griddata
# Arrays for data
lons = []
lats = []
pressure = []
temp = []
# Read data
f = open('/opt/graphics/cfplot_data/synop_data.txt')
lines = f.readlines()
for line in lines:
mysplit = line.split()
lons = np.append(lons, float(mysplit[1]))
lats = np.append(lats, float(mysplit[2]))
pressure = np.append(pressure, float(mysplit[3]))
temp = np.append(temp, float(mysplit[4]))
# Linearly interpolate data to a regular grid
lons_new = np.arange(140) * 0.1 - 11.0
lats_new = np.arange(140) * 0.1 + 49.0
temp_new = griddata(lons, lats, temp, lons_new, lats_new, interp='linear')
cscale('parula')
con(x=lons_new, y=lats_new, f=temp_new, ptype=1)
compare_images(24)
# example25
reset()
setvars(file='fig25.png')
gopen()
con(x=lons_new, y=lats_new, f=temp_new, ptype=1)
for i in np.arange(len(lines)):
plotvars.plot.text(float(lons[i]), float(lats[i]), str(temp[i]),
horizontalalignment='center',
verticalalignment='center')
gclose()
compare_images(25)
# example26
reset()
setvars(file='fig26.png')
from netCDF4 import Dataset as ncfile
from matplotlib.mlab import griddata
# Get an Orca grid and flatten the arrays
nc = ncfile('/opt/graphics/cfplot_data/orca2.nc')
lons = np.array(nc.variables['longitude'])
lats = np.array(nc.variables['latitude'])
temp = np.array(nc.variables['sst'])
lons = lons.flatten()
lats = lats.flatten()
temp = temp.flatten()
# Add wrap around at both longitude limits
pts = np.squeeze(np.where(lons < -150))
lons = np.append(lons, lons[pts] + 360)
lats = np.append(lats, lats[pts])
temp = np.append(temp, temp[pts])
pts = np.squeeze(np.where(lons > 150))
lons = np.append(lons, lons[pts] - 360)
lats = np.append(lats, lats[pts])
temp = np.append(temp, temp[pts])
lons_new = np.arange(181 * 8) * 0.25 - 180.0
lats_new = np.arange(91 * 8) * 0.25 - 90.0
temp_new = griddata(lons, lats, temp, lons_new, lats_new, interp='linear')
con(x=lons_new, y=lats_new, f=temp_new, ptype=1)
compare_images(26)
# example27
reset()
setvars(file='fig27.png')
f = cf.read('/opt/graphics/cfplot_data/ggap.nc')[1]
g = f.collapse('X: mean')
lineplot(g.subspace(pressure=100), marker='o', color='blue',
title='Zonal mean zonal wind at 100mb')
compare_images(27)
# example28
reset()
setvars(file='fig28.png')
f = cf.read('/opt/graphics/cfplot_data/ggap.nc')[1]
g = f.collapse('X: mean')
xticks = [-90, -75, -60, -45, -30, -15, 0, 15, 30, 45, 60, 75, 90]
xticklabels = ['90S', '75S', '60S', '45S', '30S', '15S', '0', '15N',
'30N', '45N', '60N', '75N', '90N']
xpts = [-30, 30, 30, -30, -30]
ypts = [-8, -8, 5, 5, -8]
gset(xmin=-90, xmax=90, ymin=-10, ymax=50)
gopen()
lineplot(g.subspace(pressure=100), marker='o', color='blue',
title='Zonal mean zonal wind', label='100mb')
lineplot(g.subspace(pressure=200), marker='D', color='red',
label='200mb', xticks=xticks, xticklabels=xticklabels,
legend_location='upper right')
plotvars.plot.plot(xpts, ypts, linewidth=3.0, color='green')
plotvars.plot.text(35, -2, 'Region of interest',
horizontalalignment='left')
gclose()
compare_images(28)
# example29
reset()
setvars(file='fig29.png')
f = cf.read('/opt/graphics/cfplot_data/tas_A1.nc')[0]
temp = f.subspace(time=cf.wi(cf.dt('1900-01-01'), cf.dt('1980-01-01')))
temp_annual = temp.collapse('T: mean', group=cf.Y())
temp_annual_global = temp_annual.collapse('area: mean', weights='area')
temp_annual_global.Units -= 273.15
lineplot(
temp_annual_global,
title='Global average annual temperature',
color='blue')
compare_images(29)
[docs]
def compare_images(example=None):
"""
| Compare images and return an error string if they don't match
|
|
|
|
|
|
|
"""
import hashlib
disp = which('display')
conv = which('convert')
comp = which('compare')
file = 'fig' + str(example) + '.png'
file_new = '/home/andy/cfplot.src/cfplot/' + file
file_ref = '/home/andy/regression/' + file
# Check md5 checksums are the same and display files if not
if hashlib.md5(open(file_new, 'rb').read()).hexdigest() != hashlib.md5(
open(file_ref, 'rb').read()).hexdigest():
print('***Failed example ' + str(example) + '**')
error_image = '/home/andy/cfplot.src/cfplot/' + 'error_' + file
diff_image = '/home/andy/cfplot.src/cfplot/' + 'difference_' + file
p = subprocess.Popen([comp, file_new, file_ref, diff_image])
(output, err) = p.communicate()
p.wait()
p = subprocess.Popen([conv, "+append", file_new,
file_ref, error_image])
(output, err) = p.communicate()
p.wait()
subprocess.Popen([disp, diff_image])
else:
print('Passed example ' + str(example))
[docs]
def compare_arrays(ref=None, levs_test=None, gvals_test=None,
mapaxis_test=None, min=None, max=None, step=None,
mult=None, type=None):
"""
| Compare arrays and return an error string if they don't match
|
|
|
|
|
|
|
"""
anom = 0
if levs_test:
levs(min, max, step)
if np.size(ref) != np.size(plotvars.levels):
anom = 1
else:
for val in np.arange(np.size(ref)):
if abs(ref[val] - plotvars.levels[val]) >= 1e-6:
anom = 1
if anom == 1:
print('***levs failure***')
print('min, max, step are', min, max, step)
print('generated levels are:')
print(plotvars.levels)
print('expected levels:')
print(ref)
else:
pass_str = 'Passed cfp.levs(min=' + str(min) + ', max='
pass_str += str(max) + ', step=' + str(step) + ')'
print(pass_str)
anom = 0
if gvals_test:
vals, testmult = gvals(min, max)
if np.size(ref) != np.size(vals):
anom = 1
else:
for val in np.arange(np.size(ref)):
if abs(ref[val] - vals[val]) >= 1e-6:
anom = 1
if mult != testmult:
anom = 1
if anom == 1:
print('***gvals failure***')
print('cfp.gvals(' + str(min) + ', ' + str(max) + ')')
print('')
print('generated values are:', vals)
print('with a multiplier of ', testmult)
print('')
print('expected values:', ref)
print('with a multiplier of ', mult)
else:
pass_str = 'Passed cfp.gvals(' + str(min) + ', ' + str(max) + ')'
print(pass_str)
anom = 0
if mapaxis_test:
ref_ticks = ref[0]
ref_labels = ref[1]
test_ticks, test_labels = mapaxis(min=min, max=max, type=type)
if np.size(test_ticks) != np.size(ref_ticks):
anom = 1
else:
for val in np.arange(np.size(ref_ticks)):
if abs(ref_ticks[val] - test_ticks[val]) >= 1e-6:
anom = 1
if ref_labels[val] != test_labels[val]:
anom = 1
if anom == 1:
print('***mapaxis failure***')
print('')
print('cfp.mapaxis(min=' + str(min) + ', max=' + str(max))
print(', type=' + str(type) + ')')
print('generated values are:', test_ticks)
print('with labels:', test_labels)
print('')
print('expected ticks:', ref_ticks)
print('with labels:', ref_labels)
else:
pass_str = 'Passed cfp.mapaxis(min=' + str(min) + ', max='
pass_str += str(max) + ', type=' + str(type) + ')'
print(pass_str)
[docs]
def traj(f=None, title=None, ptype=0, linestyle='-', linewidth=1.0, linecolor='b',
marker='o', markevery=1, markersize=5.0, markerfacecolor='r',
markeredgecolor='g', markeredgewidth=1.0, latmax=None, latmin=None,
axes=True, xaxis=True, yaxis=True,
verbose=None, legend=False, legend_lines=False,
xlabel=None, ylabel=None, xticks=None, yticks=None,
xticklabels=None, yticklabels=None, colorbar=None,
colorbar_position=None, colorbar_orientation='horizontal',
colorbar_title=None, colorbar_text_up_down=False,
colorbar_text_down_up=False, colorbar_drawedges=True,
colorbar_fraction=None, colorbar_thick=None,
colorbar_anchor=None, colorbar_shrink=None,
colorbar_labels=None,
vector=False, head_width=0.4, head_length=1.0,
fc='k', ec='k', zorder=None):
"""
| traj is the interface to trajectory plotting in cf-plot.
| The minimum use is traj(f) where f is a CF field.
|
| f - CF data used to make a line plot
| linestyle='-' - line style
| linecolor='b' - line colour
| linewidth=1.0 - line width
| marker='o' - marker for points along the line
| markersize=30 - size of the marker
| markerfacecolor='b' - colour of the marker face
| markeredgecolor='g' - colour of the marker edge
| legend=False - plot different colour markers based on a set of user levels
| zorder=None - order for plotting
| verbose=None - Set to True to get a verbose listing of what traj is doing
|
| The following parameters override any CF data defaults:
| title=None - plot title
| axes=True - plot x and y axes
| xaxis=True - plot xaxis
| yaxis=True - plot y axis
| xlabel=None - x name
| ylabel=None - y name
| xticks=None - x ticks
| xticklabels=None - x tick labels
| yticks=None - y ticks
| yticklabels=None - y tick labels
| colorbar=None - plot a colorbar
| colorbar_position=None - position of colorbar
| [xmin, ymin, x_extent,y_extent] in normalised
| coordinates. Use when a common colorbar
| is required for a set of plots. A typical set
| of values would be [0.1, 0.05, 0.8, 0.02]
| colorbar_orientation=None - orientation of the colorbar
| colorbar_title=None - title for the colorbar
| colorbar_text_up_down=False - if True horizontal colour bar labels alternate
| above (start) and below the colour bar
| colorbar_text_down_up=False - if True horizontal colour bar labels alternate
| below (start) and above the colour bar
| colorbar_drawedges=True - draw internal divisions in the colorbar
| colorbar_fraction=None - space for the colorbar - default = 0.21, in normalised
| coordinates
| colorbar_thick=None - thickness of the colorbar - default = 0.015, in normalised
| coordinates
| colorbar_anchor=None - default=0.5 - anchor point of colorbar within the fraction space.
| 0.0 = close to plot, 1.0 = further away
| colorbar_shrink=None - value to shrink the colorbar by. If the colorbar
| exceeds the plot area then values of 1.0, 0.55
| or 0.5m ay help it better fit the plot area.
| colorbar_labels=None - labels for the colorbar. Default is to use the levels defined
| using cfp.levs
| Vector options
| vector=False - Draw vectors
| head_width=2.0 - vector head width
| head_length=2.0 - vector head length
| fc='k' - vector face colour
| ec='k' - vector edge colour
"""
if verbose:
print('traj - making a trajectory plot')
if isinstance(f, cf.FieldList):
errstr = "\n\ncfp.traj - cannot make a trajectory plot from a field list "
errstr += "- need to pass a field\n\n"
raise TypeError(errstr)
# Read in data
# Find the auxiliary lons and lats if provided
has_lons = False
has_lats = False
for mydim in list(f.auxiliary_coordinates()):
name = cf_var_name(field=f, dim=mydim)
if name in ['longitude']:
lons = np.squeeze(f.construct(mydim).array)
has_lons = True
if name in ['latitude']:
lats = np.squeeze(f.construct(mydim).array)
has_lats = True
data = f.array
# Raise an error if lons and lats not found in the input data
if not has_lons or not has_lats:
message = '\n\n\ntraj error\n'
if not has_lons:
message += 'missing longitudes in the field auxiliary data\n'
if not has_lats:
message += 'missing latitudes in the field auxiliary data\n'
message += '\n\n\n'
raise TypeError(message)
if latmax is not None:
pts = np.where(lats >= latmax)
if np.size(pts) > 0:
lons[pts] = np.nan
lats[pts] = np.nan
if latmin is not None:
pts = np.where(lats <= latmin)
if np.size(pts) > 0:
lons[pts] = np.nan
lats[pts] = np.nan
# Set potential user axis labels
user_xlabel = xlabel
user_ylabel = ylabel
user_xlabel = ''
user_ylabel = ''
# Set plotting parameters
continent_thickness = 1.5
continent_color = 'k'
continent_linestyle = '-'
if plotvars.continent_thickness is not None:
continent_thickness = plotvars.continent_thickness
if plotvars.continent_color is not None:
continent_color = plotvars.continent_color
if plotvars.continent_linestyle is not None:
continent_linestyle = plotvars.continent_linestyle
land_color = plotvars.land_color
ocean_color = plotvars.ocean_color
lake_color = plotvars.lake_color
##################
# Open a new plot is necessary
##################
if plotvars.user_plot == 0:
gopen(user_plot=0)
# Call gpos(1) if not already called
if plotvars.rows > 1 or plotvars.columns > 1:
if plotvars.gpos_called is False:
gpos(1)
# Set up mapping
if plotvars.user_mapset == 0:
plotvars.lonmin = -180
plotvars.lonmax = 180
plotvars.latmin = -90
plotvars.latmax = 90
set_map()
mymap = plotvars.mymap
# Set the plot limits
gset(xmin=plotvars.lonmin, xmax=plotvars.lonmax,
ymin=plotvars.latmin, ymax=plotvars.latmax, user_gset=0)
# Make lons and lats 2d if they are 1d
ndim = np.ndim(lons)
if ndim == 1:
lons = lons.reshape(1, -1)
lats = lats.reshape(1, -1)
ntracks = np.shape(lons)[0]
if ndim == 1:
ntracks = 1
if legend or legend_lines:
# Check levels are not None
levs = plotvars.levels
if plotvars.levels is not None:
if verbose:
print('traj - plotting different colour markers based on a user set of levels')
levs = plotvars.levels
else:
# Automatic levels
if verbose:
print('traj - generating automatic legend levels')
dmin = np.nanmin(data)
dmax = np.nanmax(data)
levs, mult = gvals(dmin=dmin, dmax=dmax, mod=False)
# Add extend options to the levels if set
if plotvars.levels_extend == 'min' or plotvars.levels_extend == 'both':
levs = np.append(-1e-30, levs)
if plotvars.levels_extend == 'max' or plotvars.levels_extend == 'both':
levs = np.append(levs, 1e30)
# Set the default colour scale
if plotvars.cscale_flag == 0:
cscale('viridis', ncols=np.size(levs) + 1)
plotvars.cscale_flag = 0
# User selected colour map but no mods so fit to levels
if plotvars.cscale_flag == 1:
cscale(plotvars.cs_user, ncols=np.size(levs) + 1)
plotvars.cscale_flag = 1
##################################
# Line, symbol and vector plotting
##################################
for track in np.arange(ntracks):
xpts = lons[track, :]
ypts = lats[track, :]
data2 = data[track, :]
xpts_orig = deepcopy(xpts)
xpts = np.mod(xpts + 180, 360) - 180
# Check if xpts are only within the remapped longitudes above
if np.min(xpts) < -170 or np.max(xpts) > 170:
xpts = xpts_orig
for ix in np.arange(np.size(xpts)-1):
diff = xpts[ix+1] - xpts[ix]
if diff >= 60:
xpts[ix+1] = xpts[ix+1] - 360.0
if diff <= -60:
xpts[ix+1] = xpts[ix+1] + 360.0
# Plot lines and markers
plot_linewidth = linewidth
plot_markersize = markersize
if legend:
plot_markersize = 0.0
if plot_linewidth > 0.0 or plot_markersize > 0.0:
if verbose and track == 0 and linewidth > 0.0:
print('plotting lines')
if verbose and track == 0 and markersize > 0.0:
print('plotting markers')
if legend_lines is False:
mymap.plot(xpts, ypts, color=linecolor,
linewidth=plot_linewidth, linestyle=linestyle,
marker=marker, markevery=markevery, markersize=plot_markersize,
markerfacecolor=markerfacecolor, markeredgecolor=markeredgecolor,
markeredgewidth=markeredgewidth,
zorder=zorder, clip_on=True, transform=ccrs.PlateCarree())
else:
line_xpts = xpts.compressed()
line_ypts = ypts.compressed()
line_data = data2.compressed()
for i in np.arange(np.size(line_xpts)-1):
val = (line_data[i] + line_data[i+1])/2.0
col = plotvars.cs[np.max(np.where(val > plotvars.levels))]
mymap.plot(line_xpts[i:i+2], line_ypts[i:i+2], color=col,
linewidth=plot_linewidth, linestyle=linestyle,
zorder=zorder, clip_on=True, transform=ccrs.PlateCarree())
# Plot vectors
if vector:
if verbose and track == 0:
print('plotting vectors')
if zorder is None:
plot_zorder = 101
else:
plot_zorder = zorder
if plotvars.proj == 'cyl':
if isinstance(xpts, np.ma.MaskedArray):
pts = np.ma.MaskedArray.count(xpts)
else:
pts = xpts.size
for pt in np.arange(pts-1):
mymap.arrow(xpts[pt], ypts[pt],
xpts[pt+1] - xpts[pt],
ypts[pt+1] - ypts[pt],
head_width=head_width,
head_length=head_length,
fc=fc, ec=ec,
length_includes_head=True,
zorder=plot_zorder, clip_on=True,
transform=ccrs.PlateCarree())
# Plot different colour markers based on a user set of levels
if legend:
# For polar stereographic plots mask any points outside the plotting limb
if plotvars.proj == 'npstere':
pts = np.where(lats < plotvars.boundinglat)
if np.size(pts) > 0:
lats[pts] = np.nan
if plotvars.proj == 'spstere':
pts = np.where(lats > plotvars.boundinglat)
if np.size(pts) > 0:
lats[pts] = np.nan
for track in np.arange(ntracks):
xpts = lons[track, :]
ypts = lats[track, :]
data2 = data[track, :]
for i in np.arange(np.size(levs)-1):
color = plotvars.cs[i]
if np.ma.is_masked(data2):
pts = np.ma.where(np.logical_and(data2 >= levs[i], data2 <= levs[i+1]))
else:
pts = np.where(np.logical_and(data2 >= levs[i], data2 <= levs[i+1]))
if zorder is None:
plot_zorder = 101
else:
plot_zorder = zorder
if np.size(pts) > 0:
mymap.scatter(xpts[pts], ypts[pts],
s=markersize*15,
c=color,
marker=marker,
edgecolors=markeredgecolor,
transform=ccrs.PlateCarree(), zorder=plot_zorder)
# Axes
plot_map_axes(axes=axes, xaxis=xaxis, yaxis=yaxis,
xticks=xticks, xticklabels=xticklabels,
yticks=yticks, yticklabels=yticklabels,
user_xlabel=user_xlabel, user_ylabel=user_ylabel,
verbose=verbose)
# Coastlines
feature = cfeature.NaturalEarthFeature(name='land', category='physical',
scale=plotvars.resolution,
facecolor='none')
mymap.add_feature(feature, edgecolor=continent_color,
linewidth=continent_thickness,
linestyle=continent_linestyle)
if ocean_color is not None:
mymap.add_feature(cfeature.OCEAN, edgecolor='face', facecolor=ocean_color,
zorder=plotvars.feature_zorder)
if land_color is not None:
mymap.add_feature(cfeature.LAND, edgecolor='face', facecolor=land_color,
zorder=plotvars.feature_zorder)
if lake_color is not None:
mymap.add_feature(cfeature.LAKES, edgecolor='face', facecolor=lake_color,
zorder=plotvars.feature_zorder)
# Title
if title is not None:
map_title(title)
# Color bar
plot_colorbar = False
if colorbar is None and legend:
plot_colorbar = True
if colorbar is None and legend_lines:
plot_colorbar = True
if colorbar:
plot_colorbar = True
if plot_colorbar:
if (colorbar_title is None):
colorbar_title = 'No Name'
if hasattr(f, 'id'):
colorbar_title = f.id
nc = f.nc_get_variable(False)
if nc:
colorbar_title = f.nc_get_variable()
if hasattr(f, 'short_name'):
colorbar_title = f.short_name
if hasattr(f, 'long_name'):
colorbar_title = f.long_name
if hasattr(f, 'standard_name'):
colorbar_title = f.standard_name
if hasattr(f, 'Units'):
if str(f.Units) == '':
colorbar_title += ''
else:
colorbar_title += '(' + supscr(str(f.Units)) + ')'
levs = plotvars.levels
if colorbar_labels is not None:
levs = colorbar_labels
cbar(levs=levs, labels=levs,
orientation=colorbar_orientation,
position=colorbar_position,
text_up_down=colorbar_text_up_down,
text_down_up=colorbar_text_down_up,
drawedges=colorbar_drawedges,
fraction=colorbar_fraction,
thick=colorbar_thick,
shrink=colorbar_shrink,
anchor=colorbar_anchor,
title=colorbar_title,
verbose=verbose)
##########
# Save plot
##########
if plotvars.user_plot == 0:
gclose()
[docs]
def cbar(labels=None,
orientation=None,
position=None,
shrink=None,
fraction=None,
title=None,
fontsize=None,
fontweight=None,
text_up_down=None,
text_down_up=None,
drawedges=None,
levs=None,
thick=None,
anchor=None,
extend=None,
mid=None,
verbose=None):
"""
| cbar is the cf-plot interface to the Matplotlib colorbar routine
|
| labels - colorbar labels
| orientation - orientation 'horizontal' or 'vertical'
| position - user specified colorbar position in normalised
| plot coordinates [left, bottom, width, height]
| shrink - default=1.0 - scale colorbar along length
| fraction - default = 0.21 - space for the colorbar in
| normalised plot coordinates
| title - title for the colorbar
| fontsize - font size for the colorbar text
| fontweight - font weight for the colorbar text
| text_up_down - label division text up and down starting with up
| text_down_up - label division text down and up starting with down
| drawedges - Draw internal delimeter lines in colorbar
| levs - colorbar levels
| thick - set height of colorbar - default = 0.015,
| in normalised plot coordinates
| anchor - default=0.3 - anchor point of colorbar within the fraction space.
| 0.0 = close to plot, 1.0 = further away
| extend = None - extensions for colorbar. The default is for extensions at
| both ends.
| mid = False - label mid points of colours rather than the boundaries
| verbose = None
|
|
|
"""
if verbose:
print('con - adding a colour bar')
if fontsize is None:
fontsize = plotvars.colorbar_fontsize
if fontweight is None:
fontweight = plotvars.colorbar_fontweight
if thick is None:
thick = 0.012
if plotvars.rows == 2:
thick = 0.008
if plotvars.rows == 3:
thick = 0.005
if plotvars.rows >= 4:
thick = 0.003
if drawedges is None:
drawedges = True
if orientation is None:
orientation = 'horizontal'
if fraction is None:
fraction = 0.12
if plotvars.rows == 2:
fraction = 0.08
if plotvars.rows == 3:
fraction = 0.06
if plotvars.rows >= 4:
fraction = 0.04
if shrink is None:
shrink = 1.0
if anchor is None:
anchor = 0.3
if plotvars.plot_type > 1:
anchor = 0.5
# Code for when the user specifies nlevs to the contour command rather than
# letting cf-plot work out some levels
if type(levs) == int:
if plotvars.plot_type == 0:
myplot = plotvars.mymap
else:
myplot = plotvars.plot
from mpl_toolkits.axes_grid1 import make_axes_locatable
divider = make_axes_locatable(myplot)
if orientation == 'horizontal':
if plotvars.plot_type == 1:
cax = divider.append_axes("bottom", size="2%", pad=0.3, title=title)
else:
cax = divider.append_axes("bottom", size="2%", pad=1.0, title=title)
else:
cax = divider.append_axes("right", size="2%", pad=0.5, title=title)
plotvars.master_plot.colorbar(plotvars.image, cax=cax, orientation=orientation)
return
# Change plot position based on colorbar location
if position is None:
# Work out whether the plot is a map plot or normal plot
if (plotvars.plot_type == 1 or plotvars.plot_type == 6):
this_plot = plotvars.mymap
else:
this_plot = plotvars.plot
if plotvars.plot_type == 6 and (plotvars.proj == 'rotated' or plotvars.proj == 'UKCP'):
this_plot = plotvars.plot
l, b, w, h = this_plot.get_position().bounds
if orientation == 'horizontal':
if plotvars.plot_type > 1 or plotvars.plot == 0 or plotvars.proj not in ['cyl', 'lcc', 'moll', 'merc', 'ortho', 'robin']:
this_plot.set_position([l, b + fraction, w, h - fraction])
#if plotvars.plot_type == 1 and plotvars.proj == 'cyl':
if plotvars.plot_type == 1 and plotvars.proj in ['cyl', 'lcc', 'moll', 'merc', 'ortho', 'robin']:
# Move plot up if aspect ratio is < 1.5
lonrange = plotvars.lonmax - plotvars.lonmin
latrange = plotvars.latmax - plotvars.latmin
if (lonrange / latrange) <= 1.5:
this_plot.set_position([l, b + 0.08, w, h - 0.12])
l, b, w, h = this_plot.get_position().bounds
ax1 = plotvars.master_plot.add_axes([l + w * (1.0 - shrink)/2.0,
b - fraction * (1.0 - anchor),
w * shrink,
thick])
if plotvars.plot_type > 1 or plotvars.plot_type == 0:
this_plot.set_position([l, b + fraction, w, h - fraction])
ax1 = plotvars.master_plot.add_axes([l + w * (1.0 - shrink)/2.0,
#b - fraction * (1.0 - anchor),
b,
w * shrink,
thick])
else:
ax1 = plotvars.master_plot.add_axes([l + w + fraction * (anchor - 1),
b + h * (1.0 - shrink) / 2.0,
thick,
h * shrink])
this_plot.set_position([l, b, w - fraction, h])
else:
# Set axes position on coords supplied by the user
ax1 = plotvars.master_plot.add_axes(position)
if levs is None:
if plotvars.levels is not None:
levs = np.array(plotvars.levels)
else:
if labels is None:
errstr = "\n\ncbar error - No levels or labels supplied \n\n"
raise TypeError(errstr)
else:
levs = np.arange(len(labels))
if labels is None:
labels = levs
# Work out colour bar labeling
lbot = levs
if text_up_down:
lbot = levs[1:][::2]
ltop = levs[::2]
if text_down_up:
lbot = levs[::2]
ltop = levs[1:][::2]
# Get the colour map
colmap = cscale_get_map()
cmap = matplotlib.colors.ListedColormap(colmap)
if extend is None:
extend = plotvars.levels_extend
ncolors = np.size(levs)
if extend == 'both' or extend == 'max':
ncolors = ncolors - 1
if type(levs) != int:
plotvars.norm = matplotlib.colors.BoundaryNorm(boundaries=levs, ncolors=ncolors)
# Change boundaries to floats
boundaries = levs.astype(float)
# Add colorbar extensions if definded by levs. Using boundaries[0]-1
# for the lower and boundaries[-1]+1 is just for the colorbar and
# has no meaning for the plot.
if (extend == 'min' or extend == 'both'):
cmap.set_under(plotvars.cs[0])
boundaries = np.insert(boundaries, 0, boundaries[0]-1)
if (extend == 'max' or extend == 'both'):
cmap.set_over(plotvars.cs[-1])
boundaries = np.insert(boundaries, len(boundaries), boundaries[-1]+1)
if mid is not None:
lbot_new = []
for i in np.arange(len(labels)):
mid_point = (lbot[i+1]-lbot[i])/2.0+lbot[i]
lbot_new.append(mid_point)
lbot = lbot_new
if type(levs) != list:
lbot = None
colorbar = matplotlib.colorbar.ColorbarBase(ax1, cmap=cmap,
norm=plotvars.norm,
extend=extend,
extendfrac='auto',
boundaries=boundaries,
ticks=lbot,
spacing='uniform',
orientation=orientation,
drawedges=drawedges)
else:
if mid is not None:
lbot_new = []
for i in np.arange(len(labels)):
mid_point = (lbot[i+1]-lbot[i])/2.0+lbot[i]
lbot_new.append(mid_point)
lbot = lbot_new
ax1 = plotvars.master_plot.add_axes(position)
colorbar = matplotlib.colorbar.ColorbarBase(
ax1, cmap=cmap,
norm=plotvars.norm,
extend=extend,
extendfrac='auto',
boundaries=boundaries,
ticks=lbot,
spacing='uniform',
orientation=orientation,
drawedges=drawedges)
colorbar.set_label(title, fontsize=fontsize,
fontweight=fontweight)
# Bug in Matplotlib colorbar labelling
# With clevs=[-1, 1, 10000, 20000, 30000, 40000, 50000, 60000]
# Labels are [0, 2, 10001, 20001, 30001, 40001, 50001, 60001]
# With a +1 near to the colorbar label
# Check for an extraneous level compared to the levs
if len(labels) > len(levs):
labels = labels[:len(levs)]
colorbar.set_ticklabels([str(i) for i in labels])
if orientation == 'horizontal':
for tick in colorbar.ax.xaxis.get_ticklines():
tick.set_visible(False)
for t in colorbar.ax.get_xticklabels():
t.set_fontsize(fontsize)
t.set_fontweight(fontweight)
else:
for tick in colorbar.ax.yaxis.get_ticklines():
tick.set_visible(False)
for t in colorbar.ax.get_yticklabels():
t.set_fontsize(fontsize)
t.set_fontweight(fontweight)
# Alternate text top and bottom on a horizontal colorbar if requested
# Use method described at:
# https://stackoverflow.com/questions/37161022/matplotlib-colorbar-
# alternating-top-bottom-labels
if text_up_down or text_down_up:
vmin = colorbar.norm.vmin
vmax = colorbar.norm.vmax
if colorbar.extend == 'min':
shift_l = 0.05
scaling = 0.95
elif colorbar.extend == 'max':
shift_l = 0.
scaling = 0.95
elif colorbar.extend == 'both':
shift_l = 0.05
scaling = 0.9
else:
shift_l = 0.
scaling = 1.0
# Print bottom tick labels
colorbar.ax.set_xticklabels(lbot)
# Print top tick labels
for ii in ltop:
colorbar.ax.text(shift_l + scaling*(ii-vmin)/(vmax-vmin),
1.5, str(ii), transform=colorbar.ax.transAxes,
va='bottom', ha='center', fontsize=fontsize,
fontweight=fontweight)
for t in colorbar.ax.get_xticklabels():
t.set_fontsize(fontsize)
t.set_fontweight(fontweight)
[docs]
def map_title(title=None, dims=False):
"""
| map_title is an internal routine to draw a title on a map plot
|
| title=None - title to put on map plot
| dim=False - draw a set of dimension titles
|
|
|
|
|
"""
boundinglat = plotvars.boundinglat
lon_0 = plotvars.lon_0
lonmin = plotvars.lonmin
lonmax = plotvars.lonmax
latmin = plotvars.latmin
latmax = plotvars.latmax
polar_range = 90-abs(boundinglat)
myprojs = ['cyl', 'robin', 'moll', 'merc']
if plotvars.proj in myprojs:
lon_mid = lonmin + (lonmax - lonmin) / 2.0
mylon = lon_mid
if dims:
mylon = lonmin
projs = [ccrs.PlateCarree, ccrs.Robinson, ccrs.Mollweide, ccrs.Mercator]
myind = myprojs.index(plotvars.proj)
#if plotvars.proj == 'cyl':
# proj = ccrs.PlateCarree(central_longitude=lon_mid)
proj = projs[myind](central_longitude=lon_mid)
mylat = latmax
xpt, ypt = proj.transform_point(mylon, mylat, ccrs.PlateCarree())
ypt = ypt + (latmax - latmin) / 40.0
if plotvars.proj == 'npstere':
mylon = lon_0 + 180
mylat = boundinglat-polar_range/15.0
proj = ccrs.NorthPolarStereo(central_longitude=lon_0)
xpt, ypt = proj.transform_point(mylon, mylat, ccrs.PlateCarree())
if dims:
mylon = lon_0 + 180
mylat = boundinglat-polar_range/15.0
xpt_mid, ypt = proj.transform_point(mylon, mylat, ccrs.PlateCarree())
mylon = lon_0 - 90
xpt, ypt_mid = proj.transform_point(mylon, mylat, ccrs.PlateCarree())
if plotvars.proj == 'spstere':
mylon = lon_0
mylat = boundinglat+polar_range/15.0
proj = ccrs.SouthPolarStereo(central_longitude=lon_0)
xpt, ypt = proj.transform_point(mylon, mylat, ccrs.PlateCarree())
if dims:
mylon = lon_0 + 0
#mylat = boundinglat-polar_range/15.0
mylat = boundinglat-polar_range/15.0
xpt_mid, ypt = proj.transform_point(mylon, mylat, ccrs.PlateCarree())
mylon = lon_0 - 90
xpt, ypt_mid = proj.transform_point(mylon, mylat, ccrs.PlateCarree())
if plotvars.proj == 'lcc':
mylon = lonmin + (lonmax - lonmin) / 2.0
if dims:
mylon = lonmin
lat_0 = 40
if latmin <= 0 and latmax <= 0:
lat_0 = 40
proj = ccrs.LambertConformal(central_longitude=plotvars.lon_0,
central_latitude=lat_0,
cutoff=plotvars.latmin)
mylat = latmax
xpt, ypt = proj.transform_point(mylon, mylat, ccrs.PlateCarree())
fontsize = plotvars.title_fontsize
if dims:
halign = 'left'
fontsize = plotvars.axis_label_fontsize
# Get plot position
this_plot = plotvars.plot
l, b, w, h = this_plot.get_position().bounds
# Shift to left
#if plotvars.plot_type == 1 and plotvars.proj !=cyl:
l = l - 0.1
this_plot.set_position([l, b, w, h])
l, b, w, h = this_plot.get_position().bounds
plotvars.plot.text(l + w , b + h, title, va='bottom',
ha=halign,
rotation='horizontal', rotation_mode='anchor',
fontsize=fontsize,
fontweight=plotvars.title_fontweight)
else:
halign = 'center'
plotvars.mymap.text(xpt, ypt, title, va='bottom',
ha=halign,
rotation='horizontal', rotation_mode='anchor',
fontsize=fontsize,
fontweight=plotvars.title_fontweight)
def dim_titles(title=None, title2=None, title3=None):
"""
| dim_titles is an internal routine to draw a set of dimension titles on a plot
|
| title=None - title to put on the plot
| title2=None - additional title
| title3=None - additional title
|
|
|
|
|
"""
# Logic for the supplied titles
# if just title is supplied:
# title - contour or line title
#
# if both title and title2 are supplied:
# title u component of title
# title2 v component of the title
#
# if title2 and title3 are supplied:
# title2 u component of title
# title3 v component of title
#
# move the plot around if title3 is None
# Get plot position
if plotvars.plot_type == 1:
this_plot = plotvars.mymap
else:
this_plot = plotvars.plot
l, b, w, h = this_plot.get_position().bounds
valign = 'bottom'
# Shift down if a cylindrical projection plot else to the left
if plotvars.plot_type == 1 and plotvars.proj != 'cyl':
l = l - 0.1
myx = 1.25
myy = 1.0
valign = 'top'
if title3 is None:
myx = 1.05
elif plotvars.plot_type == 1 and plotvars.proj == 'cyl':
lonrange = plotvars.lonmax - plotvars.lonmin
latrange = plotvars.latmax - plotvars.latmin
if (lonrange / latrange) > 1.5:
myx = 0.0
myy = 1.02
if (lonrange / latrange) > 1.2 and (lonrange / latrange) <= 1.5:
myx = 0.0
myy = 1.02
h = h - 0.015
if (lonrange / latrange) <= 1.2:
l = l - 0.1
#if title2 is not None:
# l = l - 0.1
myx = 1.05
myy = 1.0
w = w - 0.1
valign = 'top'
else:
h = h - 0.1
myx = 0.0
myy = 1.02
# Change the plot position if title3 is None
if title3 is None:
this_plot.set_position([l, b, w, h])
# Set x and y spacing depending on the label location
xspacing = 0.3
yspacing = 0.0
if myx == 1.05 or myx == 1.25:
xspacing = 0.0
yspacing = 0.2
if title is not None:
this_plot.text(myx, myy, title, va=valign,
ha='left',
fontsize=plotvars.axis_label_fontsize,
fontweight=plotvars.axis_label_fontweight,
transform=this_plot.transAxes)
if title2 is not None:
this_plot.text(myx + xspacing, myy - yspacing, title2, va=valign,
ha='left',
fontsize=plotvars.axis_label_fontsize,
fontweight=plotvars.axis_label_fontweight,
transform=this_plot.transAxes)
if title3 is not None:
this_plot.text(myx + xspacing * 2, myy - yspacing * 2, title3, va=valign,
ha='left',
fontsize=plotvars.axis_label_fontsize,
fontweight=plotvars.axis_label_fontweight,
transform=this_plot.transAxes)
[docs]
def plot_map_axes(axes=None, xaxis=None, yaxis=None,
xticks=None, xticklabels=None,
yticks=None, yticklabels=None,
user_xlabel=None, user_ylabel=None,
verbose=None):
"""
| plot_map_axes is an internal routine to draw the axes on a map plot
|
| axes=None - drawing axes
| xaxis=None - drawing x-axis
| yaxis=None - drawing x-axis
| xticks=None - user defined xticks
| xticklabels=None - user defined xtick labels
| yticks=None - user defined yticks
| yticklabels=None - user defined ytick labels
| user_xlabel=None - user defined xlabel
| user_ylabel=None - user defined ylabel
| verbose=None
|
|
|
|
|
"""
# Font definitions
axis_label_fontsize = plotvars.axis_label_fontsize
axis_label_fontweight = plotvars.axis_label_fontweight
# Map parameters
boundinglat = plotvars.boundinglat
lon_0 = plotvars.lon_0
lonmin = plotvars.lonmin
lonmax = plotvars.lonmax
latmin = plotvars.latmin
latmax = plotvars.latmax
# Cylindrical
if plotvars.proj == 'cyl':
if verbose:
print('con - adding cylindrical axes')
lonticks, lonlabels = mapaxis(
min=plotvars.lonmin, max=plotvars.lonmax, type=1)
latticks, latlabels = mapaxis(
min=plotvars.latmin, max=plotvars.latmax, type=2)
if axes:
if xaxis:
if xticks is None:
axes_plot(xticks=lonticks, xticklabels=lonlabels)
else:
if xticklabels is None:
axes_plot(xticks=xticks, xticklabels=xticks)
else:
axes_plot(xticks=xticks, xticklabels=xticklabels)
if yaxis:
if yticks is None:
axes_plot(yticks=latticks, yticklabels=latlabels)
else:
if yticklabels is None:
axes_plot(yticks=yticks, yticklabels=yticks)
else:
axes_plot(yticks=yticks, yticklabels=yticklabels)
if user_xlabel is not None:
plot.text(0.5, -0.10, user_xlabel, va='bottom',
ha='center',
rotation='horizontal', rotation_mode='anchor',
transform=plotvars.mymap.transAxes,
fontsize=axis_label_fontsize,
fontweight=axis_label_fontweight)
if user_ylabel is not None:
plot.text(-0.05, 0.50, user_ylabel, va='bottom',
ha='center',
rotation='vertical', rotation_mode='anchor',
transform=plotvars.mymap.transAxes,
fontsize=axis_label_fontsize,
fontweight=axis_label_fontweight)
# Polar stereographic
if plotvars.proj == 'npstere' or plotvars.proj == 'spstere':
if verbose:
print('con - adding stereographic axes')
mymap = plotvars.mymap
latrange = 90-abs(boundinglat)
proj = ccrs.Geodetic()
# Add
if axes:
if xaxis:
if yticks is None:
latvals = np.arange(5)*30-60
else:
latvals = np.array(yticks)
if plotvars.proj == 'npstere':
latvals = latvals[np.where(latvals >= boundinglat)]
else:
latvals = latvals[np.where(latvals <= boundinglat)]
for lat in latvals:
if abs(lat - boundinglat) > 1:
lons = np.arange(361)
lats = np.zeros(361)+lat
mymap.plot(lons, lats, color=plotvars.grid_colour,
linewidth=plotvars.grid_thickness,
linestyle=plotvars.grid_linestyle,
transform=proj)
if yaxis:
if xticks is None:
lonvals = np.arange(7)*60
else:
lonvals = xticks
for lon in lonvals:
label = mapaxis(lon, lon, 1)[1][0]
if plotvars.proj == 'npstere':
lats = np.arange(90-boundinglat)+boundinglat
else:
lats = np.arange(boundinglat+91)-90
lons = np.zeros(np.size(lats))+lon
mymap.plot(lons, lats, color=plotvars.grid_colour,
linewidth=plotvars.grid_thickness,
linestyle=plotvars.grid_linestyle,
transform=proj)
# Add longitude labels
if plotvars.proj == 'npstere':
proj = ccrs.NorthPolarStereo(central_longitude=lon_0)
pole = 90
latpt = boundinglat - latrange/40.0
else:
proj = ccrs.SouthPolarStereo(central_longitude=lon_0)
pole = -90
latpt = boundinglat + latrange / 40.0
lon_mid, lat_mid = proj.transform_point(0, pole, ccrs.PlateCarree())
if xaxis and axis_label_fontsize > 0.0:
for xtick in lonvals:
label = mapaxis(xtick, xtick, 1)[1][0]
lonr, latr = proj.transform_point(xtick, latpt, ccrs.PlateCarree())
v_align = 'center'
if lonr < 1:
h_align = 'right'
if lonr > 1:
h_align = 'left'
if abs(lonr) <= 1:
h_align = 'center'
if latr < 1:
v_align = 'top'
if latr > 1:
v_align = 'bottom'
mymap.text(lonr, latr, label, horizontalalignment=h_align,
verticalalignment=v_align,
fontsize=axis_label_fontsize,
fontweight=axis_label_fontweight, zorder=101)
# Make the plot circular by blanking off around the plot
# Find min and max of plotting region in map coordinates
lons = np.arange(360)
lats = np.zeros(np.size(lons))+boundinglat
device_coords = proj.transform_points(ccrs.PlateCarree(), lons, lats)
xmin = np.min(device_coords[:, 0])
xmax = np.max(device_coords[:, 0])
ymin = np.min(device_coords[:, 1])
ymax = np.max(device_coords[:, 1])
# blank off data past the bounding latitude
pts = np.where(device_coords[:, 0] >= 0.0)
xpts = np.append(device_coords[:, 0][pts], np.zeros(np.size(pts)) + xmax)
ypts = np.append(device_coords[:, 1][pts], device_coords[:, 1][pts][::-1])
mymap.fill(xpts, ypts, alpha=1.0, color='w', zorder=100)
xpts = np.append(np.zeros(np.size(pts)) + xmin, -1.0 * device_coords[:, 0][pts])
ypts = np.append(device_coords[:, 1][pts], device_coords[:, 1][pts][::-1])
mymap.fill(xpts, ypts, alpha=1.0, color='w', zorder=100)
# Turn off map outside the cicular plot area
#mymap.outline_patch.set_visible(False)
mymap.set_frame_on(False)
# Draw a line around the bounding latitude
lons = np.arange(361)
lats = np.zeros(np.size(lons)) + boundinglat
device_coords = proj.transform_points(ccrs.PlateCarree(), lons, lats)
mymap.plot(device_coords[:, 0], device_coords[:, 1], color='k',
zorder=100, clip_on=False)
# Modify xlim and ylim values as the default values clip the plot slightly
xmax = np.max(np.abs(mymap.set_xlim(None)))
mymap.set_xlim((-xmax, xmax), emit=False)
ymax = np.max(np.abs(mymap.set_ylim(None)))
mymap.set_ylim((-ymax, ymax), emit=False)
# Lambert conformal
if plotvars.proj == 'lcc':
lon_0 = plotvars.lonmin+(plotvars.lonmax-plotvars.lonmin)/2.0
lat_0 = plotvars.latmin+(plotvars.latmax-plotvars.latmin)/2.0
mymap = plotvars.mymap
standard_parallels = [33, 45]
if latmin <= 0 and latmax <= 0:
standard_parallels = [-45, -33]
proj = ccrs.LambertConformal(central_longitude=lon_0,
central_latitude=lat_0,
cutoff=40,
standard_parallels=standard_parallels)
lonmin = plotvars.lonmin
lonmax = plotvars.lonmax
latmin = plotvars.latmin
latmax = plotvars.latmax
# Modify xlim and ylim values as the default values clip the plot slightly
xmin = mymap.set_xlim(None)[0]
xmax = mymap.set_xlim(None)[1]
ymin = mymap.set_ylim(None)[0]
ymax = mymap.set_ylim(None)[1]
mymap.set_ylim(ymin*1.05, ymax, emit=False)
mymap.set_ylim(None)
# Mask off contours that appear because of the plot extention
# mymap.add_patch(mpatches.Polygon([[xmin, ymin], [xmax,ymin],
# [xmax, ymin*1.05], [xmin, ymin*1.05]],
# facecolor='red'))
# transform=ccrs.PlateCarree()))
lons = np.arange(lonmax-lonmin+1) + lonmin
lats = np.arange(latmax-latmin+1) + latmin
verts = []
for lat in lats:
verts.append([lonmin, lat])
for lon in lons:
verts.append([lon, latmax])
for lat in lats[::-1]:
verts.append([lonmax, lat])
for lon in lons[::-1]:
verts.append([lon, latmin])
# Mask left and right of plot
lats = np.arange(latmax-latmin+1) + latmin
lons = np.zeros(np.size(lats)) + lonmin
device_coords = proj.transform_points(ccrs.PlateCarree(), lons, lats)
xmin = np.min(device_coords[:, 0])
xmax = np.max(device_coords[:, 0])
if lat_0 > 0:
ymin = np.min(device_coords[:, 1])
ymax = np.max(device_coords[:, 1])
else:
ymin = np.max(device_coords[:, 1])
ymax = np.min(device_coords[:, 1])
# Left
mymap.fill([xmin, xmin, xmax, xmin],
[ymin, ymax, ymax, ymin],
alpha=1.0, color='w', zorder=100)
mymap.plot([xmin, xmax], [ymin, ymax], color='k', zorder=101, clip_on=False)
# Right
mymap.fill([-xmin, -xmin, -xmax, -xmin],
[ymin, ymax, ymax, ymin],
alpha=1.0, color='w', zorder=100)
mymap.plot([-xmin, -xmax], [ymin, ymax], color='k', zorder=101, clip_on=False)
# Upper
lons = np.arange(lonmax-lonmin+1) + lonmin
lats = np.zeros(np.size(lons)) + latmax
device_coords = proj.transform_points(ccrs.PlateCarree(), lons, lats)
ymax = np.max(device_coords[:, 1])
xpts = np.append(device_coords[:, 0], device_coords[:, 0][::-1])
ypts = np.append(device_coords[:, 1], np.zeros(np.size(lons))+ymax)
mymap.fill(xpts, ypts, alpha=1.0, color='w', zorder=100)
mymap.plot(device_coords[:, 0], device_coords[:, 1], color='k', zorder=101, clip_on=False)
# Lower
lons = np.arange(lonmax-lonmin+1) + lonmin
lats = np.zeros(np.size(lons)) + latmin
device_coords = proj.transform_points(ccrs.PlateCarree(), lons, lats)
ymin = np.min(device_coords[:, 1]) * 1.05
xpts = np.append(device_coords[:, 0], device_coords[:, 0][::-1])
ypts = np.append(device_coords[:, 1], np.zeros(np.size(lons))+ymin)
mymap.fill(xpts, ypts, alpha=1.0, color='w', zorder=100)
mymap.plot(device_coords[:, 0], device_coords[:, 1], color='k', zorder=101, clip_on=False)
# Turn off drawing of the rectangular box around the plot
#mymap.outline_patch.set_visible(False)
mymap.set_frame_on(False)
if lat_0 < 0:
lons = np.arange(lonmax - lonmin + 1) + lonmin
lats = np.zeros(np.size(lons)) + latmax
device_coords = proj.transform_points(ccrs.PlateCarree(), lons, lats)
xmin = np.min(device_coords[:, 0])
xmax = np.max(device_coords[:, 0])
lons = np.arange(lonmax-lonmin+1) + lonmin
lats = np.zeros(np.size(lons)) + latmin
device_coords = proj.transform_points(ccrs.PlateCarree(), lons, lats)
ymax = np.min(device_coords[:, 1])
ymin = ymax * 1.1
xpts = [xmin, xmax, xmax, xmin, xmin]
ypts = [ymin, ymin, ymax, ymax, ymin]
mymap.fill(xpts, ypts, alpha=1.0, color='w', zorder=100)
# Draw longitudes and latitudes if requested
fs = plotvars.axis_label_fontsize
fw = plotvars.axis_label_fontweight
if axes and xaxis:
if xticks is None:
map_xticks, map_xticklabels = mapaxis(min=plotvars.lonmin,
max=plotvars.lonmax, type=1)
else:
map_xticks = xticks
if xticklabels is None:
map_xticklabels = xticks
else:
map_xticklabels = xticklabels
if axes and xaxis:
lats = np.arange(latmax - latmin + 1) + latmin
for tick in np.arange(np.size(map_xticks)):
lons = np.zeros(np.size(lats)) + map_xticks[tick]
device_coords = proj.transform_points(ccrs.PlateCarree(), lons, lats)
mymap.plot(device_coords[:, 0], device_coords[:, 1],
linewidth=plotvars.grid_thickness,
linestyle=plotvars.grid_linestyle,
color=plotvars.grid_colour,
zorder=101)
latpt = latmin - 3
if lat_0 < 0:
latpt = latmax + 1
device_coords = proj.transform_point(map_xticks[tick], latpt,
ccrs.PlateCarree())
mymap.text(device_coords[0], device_coords[1],
map_xticklabels[tick],
horizontalalignment='center',
fontsize=fs,
fontweight=fw,
zorder=101)
if yticks is None:
map_yticks, map_yticklabels = mapaxis(min=plotvars.latmin,
max=plotvars.latmax,
type=2)
else:
map_yticks = yticks
if yticklabels is None:
map_yticklabels = yticks
else:
map_yticklabels = yticklabels
if axes and yaxis:
lons = np.arange(lonmax-lonmin+1) + lonmin
for tick in np.arange(np.size(map_yticks)):
lats = np.zeros(np.size(lons)) + map_yticks[tick]
device_coords = proj.transform_points(ccrs.PlateCarree(), lons, lats)
mymap.plot(device_coords[:, 0],
device_coords[:, 1],
linewidth=plotvars.grid_thickness,
linestyle=plotvars.grid_linestyle,
color=plotvars.grid_colour,
zorder=101)
device_coords = proj.transform_point(lonmin-1,
map_yticks[tick],
ccrs.PlateCarree())
mymap.text(device_coords[0],
device_coords[1],
map_yticklabels[tick],
horizontalalignment='right',
verticalalignment='center',
fontsize=fs,
fontweight=fw,
zorder=101)
device_coords = proj.transform_point(lonmax+1,
map_yticks[tick],
ccrs.PlateCarree())
mymap.text(device_coords[0],
device_coords[1],
map_yticklabels[tick],
horizontalalignment='left',
verticalalignment='center',
fontsize=fs,
fontweight=fw,
zorder=101)
# UKCP grid
if plotvars.proj == 'UKCP' and plotvars.grid:
#lonmin = -11
#lonmax = 3
#latmin = 49
#latmax = 61
#spacing = plotvars.grid_spacing
#if xticks is None:
# lons = np.arange(30 / spacing + 1) * spacing
# lons = np.append((lons*-1)[::-1], lons[1:])
#else:
# lons = xticks
#if yticks is None:
# lats = np.arange(90.0 / spacing + 1) * spacing
#else:
# lats = yticks
lons = np.arange((360/plotvars.grid_x_spacing) + 1) * plotvars.grid_x_spacing
lons = np.concatenate([lons - 360, lons])
lats = np.arange((180/plotvars.grid_y_spacing) + 1) * plotvars.grid_y_spacing - 90
if plotvars.grid:
plotvars.mymap.gridlines(color=plotvars.grid_colour,
linewidth=plotvars.grid_thickness,
linestyle=plotvars.grid_linestyle,
xlocs=lons, ylocs=lats)
[docs]
def add_cyclic(field, lons):
"""
| add_cyclic is a wrapper for cartopy_util.add_cyclic_point(field, lons)
| This is needed for the case of when the longitudes are not evenly spaced
| due to numpy rounding which causes an error from the cartopy wrapping routine.
| In this case the longitudes are promoted to 64 bit and then rounded
| to an appropriate number of decimal places before passing to the cartopy
| add_cyclic routine.
"""
try:
field, lons = cartopy_util.add_cyclic_point(field, lons)
except Exception:
ndecs_max = max_ndecs_data(lons)
lons = np.float64(lons).round(ndecs_max)
field, lons = cartopy_util.add_cyclic_point(field, lons)
return field, lons
def irregular_window(field, lons,lats):
field_irregular = deepcopy(field)
lons_irregular = deepcopy(lons)
lats_irregular = deepcopy(lats)
# Fix longitudes to be -180 to 180
# lons_irregular = ((lons_irregular + plotvars.lonmin) % 360) + plotvars.lonmin
# Test data to get appropiate longitude offset to perform remapping
found_lon = False
for ilon in [-360, 0, 360]:
lons_test = lons_irregular + ilon
if np.min(lons_test) <= plotvars.lonmin:
found_lon = True
lons_offset = ilon
if found_lon:
lons_irregular = lons_irregular + lons_offset
pts = np.where(lons_irregular < plotvars.lonmin)
lons_irregular[pts] = lons_irregular[pts] + 360.0
else:
errstr = '/n/n cf-plot error - cannot determine grid offset in add_cyclic_irregular/n/n'
raise Warning(errstr)
field_wrap = deepcopy(field_irregular)
lons_wrap = deepcopy(lons_irregular)
lats_wrap = deepcopy(lats_irregular)
delta = 120.0
pts_left = np.where(lons_wrap >= plotvars.lonmin + 360 - delta)
lons_left = lons_wrap[pts_left] - 360.0
lats_left = lats_wrap[pts_left]
field_left = field_wrap[pts_left]
field_wrap = np.concatenate([field_wrap, field_left])
lons_wrap = np.concatenate([lons_wrap, lons_left])
lats_wrap = np.concatenate([lats_wrap, lats_left])
# Make a line of interpolated data on left hand side of plot and insert this into the data
# on both the left and the right before contouring
lons_new = np.zeros(181) + plotvars.lonmin
lats_new = np.arange(181) - 90
field_new = griddata((lons_wrap, lats_wrap), field_wrap, (lons_new, lats_new), method='linear')
# Remove any non finite points in the interpolated data
pts = np.where(np.isfinite(field_new))
field_new = field_new[pts]
lons_new = lons_new[pts]
lats_new = lats_new[pts]
# Add the interpolated data to the left
field_irregular = np.concatenate([field_irregular, field_new])
lons_irregular = np.concatenate([lons_irregular, lons_new])
lats_irregular = np.concatenate([lats_irregular, lats_new])
# Add to the right if a fiull globe is being plotted
# The 359.99 here is needed or Cartopy will map 360 back to 0
if plotvars.lonmax - plotvars.lonmin == 360:
field_irregular = np.concatenate([field_irregular, field_new])
lons_irregular = np.concatenate([lons_irregular, lons_new + 359.95])
lats_irregular = np.concatenate([lats_irregular, lats_new])
else:
lons_new2 = np.zeros(181) + plotvars.lonmax
lats_new2 = np.arange(181) - 90
field_new2 = griddata((lons_wrap, lats_wrap), field_wrap, (lons_new2, lats_new2), method='linear')
# Remove any non finite points in the interpolated data
pts = np.where(np.isfinite(field_new2))
field_new2 = field_new2[pts]
lons_new2 = lons_new2[pts]
lats_new2 = lats_new2[pts]
# Add the interpolated data to the right
field_irregular = np.concatenate([field_irregular, field_new2])
lons_irregular = np.concatenate([lons_irregular, lons_new2])
lats_irregular = np.concatenate([lats_irregular, lats_new2])
# Finally remove any point off to the right of plotvars.lonmax
pts = np.where(lons_irregular <= plotvars.lonmax)
if np.size(pts) > 0:
field_irregular = field_irregular[pts]
lons_irregular = lons_irregular[pts]
lats_irregular = lats_irregular[pts]
return field_irregular, lons_irregular, lats_irregular
[docs]
def max_ndecs_data(data):
ndecs_max = 1
data_ndecs = np.zeros(len(data))
for i in np.arange(len(data)):
data_ndecs[i] = len(str(data[i]).split('.')[1])
if max(data_ndecs) >= ndecs_max:
# Reset large decimal vales to zero
if min(data_ndecs) < 10:
pts = np.where(data_ndecs >= 10)
data_ndecs[pts] = 0
ndecs_max = int(max(data_ndecs))
return ndecs_max
[docs]
def fix_floats(data):
"""
| fix_floats fixes numpy rounding issues where 0.4 becomes
| 0.399999999999999999999
|
"""
# Return unchecked if any values have an e in them, for example 7.85e-8
has_e = False
for val in data:
if 'e' in str(val):
has_e = True
if has_e:
return(data)
data_ndecs = np.zeros(len(data))
for i in np.arange(len(data)):
data_ndecs[i] = len(str(float(data[i])).split('.')[1])
if max(data_ndecs) >= 10:
# Reset large decimal vales to zero
if min(data_ndecs) < 10:
pts = np.where(data_ndecs >= 10)
data_ndecs[pts] = 0
ndecs_max = int(max(data_ndecs))
# Reset to new ndecs_max decimal places
for i in np.arange(len(data)):
data[i] = round(data[i], ndecs_max)
else:
# fix to two or more decimal places
nd = 2
data_range = 0.0
data_temp = data
while data_range == 0.0:
data_temp = deepcopy(data)
for i in np.arange(len(data_temp)):
data_temp[i] = round(data_temp[i], nd)
data_range = np.max(data_temp) - np.min(data_temp)
nd = nd + 1
data = data_temp
return(data)
[docs]
def calculate_levels(field=None, level_spacing=None, verbose=None):
dmin = np.nanmin(field)
dmax = np.nanmax(field)
tight = True
field2 = deepcopy(field)
if plotvars.user_levs == 1:
# User defined
if verbose:
print('cfp.calculate_levels - using user defined contour levels')
clevs = plotvars.levels
mult = 0
fmult = 1
else:
if plotvars.levels_step is None:
# Automatic levels
mult = 0
fmult = 1
if verbose:
print('cfp.calculate_levels - generating automatic contour levels')
if level_spacing == 'outlier' or level_spacing == 'inspect':
hist = np.histogram(field, 100)[0]
pts = np.size(field)
rate = 0.01
outlier_detected = False
if sum(hist[1:-2]) ==0:
if hist[0] / hist[-1] < rate:
outlier_detected = True
pts = np.where(field == dmin)
field2[pts] = dmax
dmin = np.nanmin(field2)
if hist[-1] / hist[0] < rate:
outlier_detected = True
pts = np.where(field == dmax)
field2[pts] = dmin
dmax = np.nanmax(field2)
clevs, mult = gvals(dmin=dmin, dmax=dmax)
fmult = 10**-mult
tight = False
if level_spacing == 'linear':
if isinstance(np.ma.min(dmin), np.ma.core.MaskedConstant) or \
isinstance(np.ma.min(dmax), np.ma.core.MaskedConstant):
errstr = 'cf-plot calculate_levels error - data is entirely masked\n'
errstr += 'setting levels to 0 and 0.1 to produce a plot'
print(errstr)
dmin = 0.0
dmax = 0.1
#if dmax - dmin < 1e-12:
# errstr = 'cf-plot calculate_levels error - field difference is < 1e-12\n'
# errstr += 'setting levels to min-0.1 and min+0.1 to produce a plot'
# print(errstr)
# dmin = dmin - 1
# dmax = dmin + 1
clevs, mult = gvals(dmin=dmin, dmax=dmax)
fmult = 10**-mult
tight = False
if level_spacing == 'log' or level_spacing == 'loglike':
if dmin < 0.0 and dmax < 0.0:
dmin1 = abs(dmax)
dmax1 = abs(dmin)
if dmin > 0.0 and dmax > 0.0:
dmin1 = abs(dmin)
dmax1 = abs(dmax)
if dmin <= 0.0 and dmax >= 0.0:
dmax1 = max(abs(dmin), dmax)
pts = np.where(field < 0.0)
close_below = np.max(field[pts])
pts = np.where(field > 0.0)
close_above = np.min(field[pts])
dmin1 = min(abs(close_below), close_above)
# Generate levels
if level_spacing == 'log':
clevs = []
for i in np.arange(31):
val = 10**(i-30.)
clevs.append("{:.0e}".format(val))
if level_spacing == 'loglike':
clevs = []
for i in np.arange(61):
val = 10**(i-30.)
clevs.append("{:.0e}".format(val))
clevs.append("{:.0e}".format(val*2))
clevs.append("{:.0e}".format(val*5))
clevs = np.float64(clevs)
# Remove out of range levels
clevs = np.float64(clevs)
pts = np.where(np.logical_and(clevs >= abs(dmin1), clevs <= abs(dmax1)))
clevs = clevs[pts]
if dmin < 0.0 and dmax < 0.0:
clevs = -1.0*clevs[::-1]
if dmin <= 0.0 and dmax >= 0.0:
clevs = np.concatenate([-1.0*clevs[::-1], [0.0], clevs])
# Use step to generate the levels
if plotvars.levels_step is not None:
if verbose:
print('calculate_levels - using specified step to generate contour levels')
step = plotvars.levels_step
if isinstance(step, int):
dmin = int(dmin)
dmax = int(dmax)
fmult = 1
mult = 0
clevs = []
if dmin < 0:
clevs = ((np.arange(-1*dmin/step+1)*-step)[::-1])
if dmax > 0:
if np.size(clevs) > 0:
clevs = np.concatenate((clevs[:-1], np.arange(dmax/step+1)*step))
else:
clevs = np.arange(dmax/step+1)*step
if isinstance(step, int):
clevs = clevs.astype(int)
# Remove any out of data range values
if tight:
pts = np.where(np.logical_and(clevs >= dmin, clevs <= dmax))
clevs = clevs[pts]
# Add an extra contour level if less than two levels are present
if np.size(clevs) < 2:
clevs.append(clevs[0]+0.001)
# Test for large numer of decimal places and fix if necessary
if plotvars.levels is None:
if isinstance(clevs[0], float):
clevs = fix_floats(clevs)
return(clevs, mult, fmult)
[docs]
def stream(u=None, v=None, x=None, y=None, density=None, linewidth=None,
color=None, arrowsize=None, arrowstyle=None, minlength=None,
maxlength=None, axes=True,
xaxis=True, yaxis=True, xticks=None, xticklabels=None, yticks=None,
yticklabels=None, xlabel=None, ylabel=None, title=None,
zorder=None):
"""
| stream - plot a streamplot which is used to show fluid flow and 2D field gradients
|
| u=None - u wind
| v=None - v wind
| x=None - x locations of u and v
| y=None - y locations of u and v
| density=None - controls the closeness of streamlines. When density = 1,
| the domain is divided into a 30x30 grid
| linewidth=None - the width of the stream lines. With a 2D array the line width
| can be varied across the grid. The array must have the same shape
| as u and v
| color=None - the streamline color
| arrowsize=None - scaling factor for the arrow size
| arrowstyle=None - arrow style specification
| minlength=None - minimum length of streamline in axes coordinates
| maxlength=None - maximum length of streamline in axes coordinates
| axes=True - plot x and y axes
| xaxis=True - plot xaxis
| yaxis=True - plot y axis
| xticks=None - xtick positions
| xticklabels=None - xtick labels
| yticks=None - y tick positions
| yticklabels=None - ytick labels
| xlabel=None - label for x axis
| ylabel=None - label for y axis
| title=None - title for plot
| zorder=None - plotting order
|
:Returns:
None
|
|
|
"""
colorbar_title = ''
if title is None:
title = ''
text_fontsize = plotvars.text_fontsize
continent_thickness = plotvars.continent_thickness
continent_color = plotvars.continent_color
if text_fontsize is None:
text_fontsize = 11
if continent_thickness is None:
continent_thickness = 1.5
if continent_color is None:
continent_color = 'k'
title_fontsize = plotvars.title_fontsize
if title_fontsize is None:
title_fontsize = 15
resolution_orig = plotvars.resolution
rotated_vect = False
# Set potential user axis labels
user_xlabel = xlabel
user_ylabel = ylabel
# Set any additional arguments to streamplot
plotargs = {}
if density is not None:
plotargs['density'] = density
if linewidth is not None:
plotargs['linewidth'] = linewidth
if color is not None:
plotargs['color'] = color
if arrowsize is not None:
plotargs['arrowsize'] = arrowsize
if arrowstyle is not None:
plotargs['arrowstyle'] = arrowstyle
if minlength is not None:
plotargs['minlength'] = minlength
if maxlength is not None:
plotargs['maxlength'] = maxlength
# Extract required data
# If a cf-python field
if isinstance(u, cf.Field):
# Check data is 2D
ndims = np.squeeze(u.data).ndim
if ndims != 2:
errstr = "\n\ncfp.vect error need a 2 dimensonal u field to make vectors\n"
errstr += "received " + str(np.squeeze(u.data).ndim)
if ndims == 1:
errstr += " dimension\n\n"
else:
errstr += " dimensions\n\n"
raise TypeError(errstr)
u_data, u_x, u_y, ptype, colorbar_title, xlabel, ylabel, xpole, \
ypole = cf_data_assign(u, colorbar_title, rotated_vect=rotated_vect)
elif isinstance(u, cf.FieldList):
raise TypeError("Can't plot a field list")
else:
# field=f #field data passed in as f
check_data(u, x, y)
u_data = deepcopy(u)
u_x = deepcopy(x)
u_y = deepcopy(y)
xlabel = ''
ylabel = ''
if isinstance(v, cf.Field):
# Check data is 2D
ndims = np.squeeze(v.data).ndim
if ndims != 2:
errstr = "\n\ncfp.vect error need a 2 dimensonal v field to make vectors\n"
errstr += "received " + str(np.squeeze(v.data).ndim)
if ndims == 1:
errstr += " dimension\n\n"
else:
errstr += " dimensions\n\n"
raise TypeError(errstr)
v_data, v_x, v_y, ptype, colorbar_title, xlabel, ylabel, xpole, \
ypole = cf_data_assign(v, colorbar_title, rotated_vect=rotated_vect)
elif isinstance(v, cf.FieldList):
raise TypeError("Can't plot a field list")
else:
# field=f #field data passed in as f
check_data(v, x, y)
v_data = deepcopy(v)
xlabel = ''
ylabel = ''
# Reset xlabel and ylabel values with user defined labels in specified
if user_xlabel is not None:
xlabel = user_xlabel
if user_ylabel is not None:
ylabel = user_ylabel
# Retrieve any user defined axis labels
if xlabel == '' and plotvars.xlabel is not None:
xlabel = plotvars.xlabel
if ylabel == '' and plotvars.ylabel is not None:
ylabel = plotvars.ylabel
if xticks is None and plotvars.xticks is not None:
xticks = plotvars.xticks
if plotvars.xticklabels is not None:
xticklabels = plotvars.xticklabels
else:
xticklabels = list(map(str, xticks))
if yticks is None and plotvars.yticks is not None:
yticks = plotvars.yticks
if plotvars.yticklabels is not None:
yticklabels = plotvars.yticklabels
else:
yticklabels = list(map(str, yticks))
# Open a new plot if necessary
if plotvars.user_plot == 0:
gopen(user_plot=0)
# Call gpos(1) if not already called
if plotvars.rows > 1 or plotvars.columns > 1:
if plotvars.gpos_called is False:
gpos(1)
# Set plot type if user specified
if (ptype is not None):
plotvars.plot_type = ptype
lonrange = np.nanmax(u_x) - np.nanmin(u_x)
latrange = np.nanmax(u_y) - np.nanmin(u_y)
if plotvars.plot_type == 1:
# Set up mapping
if (lonrange > 350 and latrange > 170) or plotvars.user_mapset == 1:
set_map()
else:
mapset(lonmin=np.nanmin(u_x), lonmax=np.nanmax(u_x),
latmin=np.nanmin(u_y), latmax=np.nanmax(u_y),
user_mapset=0, resolution=resolution_orig)
set_map()
mymap = plotvars.mymap
# Map streamplot
if plotvars.plot_type == 1:
plotvars.mymap.streamplot(u_x, u_y, u_data, v_data,
transform=ccrs.PlateCarree(),
**plotargs)
# axes
plot_map_axes(axes=axes, xaxis=xaxis, yaxis=yaxis,
xticks=xticks, xticklabels=xticklabels,
yticks=yticks, yticklabels=yticklabels,
user_xlabel=user_xlabel, user_ylabel=user_ylabel,
verbose=False)
# Coastlines
continent_thickness = plotvars.continent_thickness
continent_color = plotvars.continent_color
continent_linestyle = plotvars.continent_linestyle
if continent_thickness is None:
continent_thickness = 1.5
if continent_color is None:
continent_color = 'k'
if continent_linestyle is None:
continent_linestyle = 'solid'
feature = cfeature.NaturalEarthFeature(
name='land', category='physical',
scale=plotvars.resolution,
facecolor='none')
mymap.add_feature(feature, edgecolor=continent_color,
linewidth=continent_thickness,
linestyle=continent_linestyle)
# Title
if title is not None:
map_title(title)
##########
# Save plot
##########
if plotvars.user_plot == 0:
gset()
cscale()
gclose()
if plotvars.user_mapset == 0:
mapset()
mapset(resolution=resolution_orig)
def bfill_ugrid(f=None, face_lons=None, face_lats=None, face_connectivity=None, clevs=None,
alpha=None, zorder=None):
"""
| bfill_ugrid - block fill a irregular field with colour rectangles
| This is an internal routine and is not generally used by the user.
|
| f=None - field
| face_lons=None - longitude points for face vertices
| face_lats=None - latitude points for face verticies
| face_connectivity=None - connectivity for face verticies
| clevs=None - levels for filling
| lonlat=False - lonlat data
| bound=False - x and y are cf data boundaries
| alpha=alpha - transparency setting 0 to 1
| zorder=None - plotting order
|
:Returns:
None
|
|
|
|
"""
# Colour faces according to value
# Set faces to white initially
cols = ['#000000' for x in range(len(face_connectivity))]
levs = deepcopy(np.array(clevs))
if plotvars.levels_extend == 'min' or plotvars.levels_extend == 'both':
levs = np.concatenate([[-1e20], levs])
ilevs_max = np.size(levs)
if plotvars.levels_extend == 'max' or plotvars.levels_extend == 'both':
levs = np.concatenate([levs, [1e20]])
else:
ilevs_max = ilevs_max - 1
for ilev in np.arange(ilevs_max):
lev = levs[ilev]
col = plotvars.cs[ilev]
pts = np.where(f.squeeze() >= lev)[0]
if len(pts) > 0:
if np.min(pts) >= 0:
for val in np.arange(np.size(pts)):
pt = pts[val]
cols[pt]=col
plotargs = {'transform': ccrs.PlateCarree()}
coords_all = []
nfaces = np.shape(face_connectivity)[0]
coords_all = []
for iface in np.arange(nfaces):
lons = face_lons[iface, :]
lats = face_lats[iface, :]
# Wrapping in longitude
if (np.max(lons) - np.min(lons)) > 100:
if np.max(lons) > 180:
for j in np.arange(len(lons)):
lons[j] = (lons[j] + 180) % 360 - 180
else:
for j in np.arange(len(lons)):
lons[j] = lons[j] % 360
nverts = len(lons)
# Add extra verticies if any of the points are at the north or south pole
if np.max(lats) == 90 or np.min(lats) == -90:
geom = sgeom.Polygon([(lons[k], lats[k]) for k in np.arange(nverts)])
geom_cyl = ccrs.PlateCarree().project_geometry(geom, ccrs.Geodetic())
# Original method for shapely < 2.0
# coords = geom_cyl[0].exterior.coords[:]
# New method for shapely 2.0 +
poly_mapped = sgeom.mapping(geom_cyl.geoms[0])
coords = list(poly_mapped['coordinates'][0])
else:
coords = [(lons[k], lats[k]) for k in np.arange(nverts)]
coords_all.append(coords)
plotvars.mymap.add_collection(PolyCollection(coords_all, facecolors=cols, edgecolors=None,
alpha=alpha, zorder=zorder, **plotargs))
def generate_titles(f=None):
'''Generate a set of title dims to put at the top of plots'''
mycoords = find_dim_names(f)
well_formed = check_well_formed(f)
title_dims = ''
if isinstance(f, cf.Field):
for idim in np.arange(len(mycoords)):
mycoord = mycoords[idim]
if mycoord == 'Z':
mycoord = find_z(f)
title, units = cf_var_name_titles(f, mycoord)
if not f.coord(mycoord).T:
values = f.construct(mycoord).array
if len(values) > 1:
value = ''
else:
value = str(values)
title_dims += mycoord + ': ' + title + ' ' + value + ' ' + units + '\n'
else:
#if well_formed:
# values = f.construct(mycoord).dtarray
#else:
# values = f.construct(mycoord).array
values = f.construct(mycoord).dtarray
if len(values) > 1:
value = ''
else:
value = str(cf.Data(values).datetime_as_string)
title_dims += mycoord + ': ' + title + ' ' + value + '\n'
if len(f.cell_methods()) > 0:
title_dims += 'cell_methods: '
i = 0
for method in f.cell_methods():
if len(f.cell_methods()[method].get_axes()) > 0:
axis = f.cell_methods()[method].get_axes()[0]
try:
# Change domainaxis0 etc to an axis
myid = f.constructs.domain_axis_identity(axis)
except:
myid = axis
value = ''
if f.cell_methods()[method].has_method():
value = f.cell_methods()[method].get_method()
qualifiers = f.cell_methods()[method].qualifiers()
qualifier_text = ''
if len(qualifiers) > 0:
qualifier_text = str(qualifiers)
if i > 0:
title_dims += ', '
title_dims += myid + ': ' + value + ' ' + qualifier_text
i += 1
return title_dims
def check_well_formed(field):
'''
Check the coordinates are all recognizably of the form X, Y, Z, T
returns boolean
'''
coords = list(field.coords())
mycoords = deepcopy(coords)
for i in np.arange(len(coords)):
c = field.coord(coords[i])
if c.X:
mycoords[i] = 'X'
if c.Y:
mycoords[i] = 'Y'
if c.Z:
mycoords[i] = 'Z'
if c.T:
mycoords[i] = 'T'
# Check if the coordtinates are all of the form X, Y, Z, T
well_formed = True
dimension_coords = ['dimensioncoordinate0','dimensioncoordinate1','dimensioncoordinate2','dimensioncoordinate3']
for i in np.arange(4):
if dimension_coords[i] in mycoords:
well_formed = False
return well_formed
def find_dim_names(field):
''' Find the field dimension coordinate names
Ignores auxiliary coordinates (for now)
returns:
coordinates in the order [T, X, Y, Z]
'''
# Get the field domain axes
daxes = list(field.get_data_axes())
# Get the field coordinates
dcoords = list(field.coords())
# Calculate the number of coordinates of type X, Y, Z and T
nx = 0
ny = 0
nz = 0
nt = 0
for i in np.arange(len(dcoords)):
if field.coord(dcoords[i]).X:
nx += 1
if field.coord(dcoords[i]).Y:
ny += 1
if field.coord(dcoords[i]).Z:
nz += 1
if field.coord(dcoords[i]).T:
#print('ajh - t found - ', coords[i])
nt += 1
#print('ajh - find_dim_names - nx, ny, nz, nt are ', nx, ny, nz, nt)
# Strip out any auxiliary coordinates if the field is not a trajectory field
#remove_aux = True
#if field.get_property('featureType', False) is not False:
# if field.featureType == 'trajectory':
# remove_aux = False
# New test
remove_aux = False
# Strip out any auxiliary coordinates if the field is not a trajectory field
if remove_aux:
for i in np.arange(len(dcoords)):
if dcoords[i][:-1] == 'auxiliarycoordinate':
dcoords[i] = 'aux'
dcoords = list(filter(('aux').__ne__,dcoords))
#print('ajh - daxes are', daxes)
#print('ajh - dcoords are', dcoords)
# Convert these into corresponding dimension coordinates
if remove_aux:
coords = []
for i in np.arange(len(daxes)):
val = daxes[i]
coord = None
for j in np.arange(len(dcoords)):
#print(ajh - daxes[i], dcoords[j], field.get_data_axes(dcoords[j])[0])
if daxes[i] == field.get_data_axes(dcoords[j])[0]:
coord = dcoords[j]
if coord is not None:
coords.append(coord)
else:
errstr = 'find_data_names error - cannot find a coordinate for ' + daxes[i] + '\n'
errstr += 'in the data\n'
raise Warning(errstr)
else:
coords = dcoords
#print('ajh - coords are', coords)
#print('ajh - dcoords are', dcoords)
# Make a copy of coords in mycoords
mycoords = deepcopy(coords)
# Convert to X, Y, Z, T if coordinate is one of these
# If the number of coordinates of this type is greater than 1 then don't do this as f.coord('Z') gives an
# error as there are more that one coordinates to return
for i in np.arange(len(daxes)):
if field.coord(coords[i]).X:
if nx == 1:
mycoords[i] = 'X'
if field.coord(coords[i]).Y:
if ny == 1:
mycoords[i] = 'Y'
if field.coord(coords[i]).Z:
if nz == 1:
mycoords[i] = 'Z'
if field.coord(coords[i]).T:
if nt == 1:
mycoords[i] = 'T'
# Return the reverse of the coordinates so that they are in the order [X, Y, Z, T]
mycoords.reverse()
#print('ajh - find_dim_names - mycoords are ', mycoords)
return mycoords
def find_z(f):
''' Find the Z coordinate if it exists'''
# Return if f is undefined
if f is None:
return None
myz ='Z'
z_count = 0
z_names =[]
mycoords = find_dim_names(f)
myz = None
for mycoord in mycoords:
if f.coord(mycoord).Z:
myz = mycoord
#if myz is None:
# errstr = 'cf-plot error - cannot find the Z coordinate'
# raise Warning(errstr)
return myz
def orca_check(x, verbose=False):
''' Check input data to see if it is an orca ocean grid
We look for a single discontinuity in longitude where the data changes by
greater that 120 degrees.'''
lons = deepcopy(x)
# Only check for longitude range > 350 degrees
if np.max(lons) - np.min(lons) < 350:
return False
nvpts = np.shape(lons)[0]
lons_lower = lons[int(nvpts/4), :]
discont_lower_idx = np.where(abs(np.diff(lons_lower)) > 120)
discont_lower = lons_lower[discont_lower_idx]
lons_mid = lons[int(nvpts/2), :]
discont_mid_idx = np.where(abs(np.diff(lons_mid)) > 120)
discont_mid = lons_mid[discont_mid_idx]
lons_upper = lons[int(nvpts*3/4), :]
discont_upper_idx = np.where(abs(np.diff(lons_upper)) > 120)
discont_mid = lons_mid[discont_mid_idx]
# Check for one discontinuity
retval = False
if np.size(discont_lower_idx) == 1 and np.size(discont_mid_idx) == 1 and np.size(discont_upper_idx) == 1:
if verbose:
print('orca_check - one discontinutity')
print(discont_lower_idx, discont_mid_idx, discont_upper_idx)
v1 = float(discont_lower_idx[0])
v2 = float(discont_mid_idx[0])
v3 = float(discont_upper_idx[0])
spread = np.max(np.abs(np.diff([v1, v2, v3])))
if verbose:
print('orca_check spread is ', np.max(np.abs(np.diff([v1, v2, v3]))))
# Check for discontinuity spead of less than 20 places
if spread <= 20:
retval = True
return retval
def map_grid():
''' Plot a grid on a map '''
lons = np.arange((360/plotvars.grid_x_spacing) + 1) * plotvars.grid_x_spacing
lons = np.concatenate([lons - 360, lons])
lats = np.arange((180/plotvars.grid_y_spacing) + 1) * plotvars.grid_y_spacing - 90
plotvars.mymap.gridlines(color=plotvars.grid_colour,
linewidth=plotvars.grid_thickness,
linestyle=plotvars.grid_linestyle,
xlocs=lons, ylocs=lats, zorder=plotvars.grid_zorder)